├── .gitignore ├── CMakeLists.txt ├── Dockerfile ├── LICENSE ├── README.md ├── environment.yml ├── evaluation ├── evaluate_DREAM.ipynb └── evaluate_baxter_dataset.ipynb ├── imageloaders ├── DREAM.py └── baxter.py ├── images ├── baxter.png └── panda.jpg ├── inference_single_image.ipynb ├── models ├── BPnP.py ├── CtRNet.py ├── keypoint_seg_resnet.py ├── mesh_renderer.py └── robot_arm.py ├── package.xml ├── ros_nodes ├── baxter_leftarm_pose.py └── panda_pose.py ├── setup.py ├── train.py ├── urdfs ├── Baxter │ ├── E0 │ │ ├── E0.obj │ │ └── material.lib │ ├── E1 │ │ ├── E1.obj │ │ └── material.lib │ ├── S0 │ │ ├── S0.obj │ │ └── material.lib │ ├── S1 │ │ ├── S1.obj │ │ └── material.lib │ ├── W0 │ │ ├── W0.obj │ │ └── material.lib │ ├── W1 │ │ ├── W1.obj │ │ └── material.lib │ ├── W2 │ │ ├── W2.obj │ │ └── material.lib │ └── baxter_description │ │ ├── CMakeLists.txt │ │ ├── meshes │ │ ├── base │ │ │ ├── PEDESTAL.DAE │ │ │ ├── PEDESTAL.STL │ │ │ ├── pedestal_link_collision.DAE │ │ │ └── pedestal_link_collision.STL │ │ ├── head │ │ │ ├── H0.DAE │ │ │ ├── H0.STL │ │ │ ├── H1.DAE │ │ │ └── H1.STL │ │ ├── lower_elbow │ │ │ ├── E1.DAE │ │ │ └── E1.STL │ │ ├── lower_forearm │ │ │ ├── W1.DAE │ │ │ └── W1.STL │ │ ├── lower_shoulder │ │ │ ├── S1.DAE │ │ │ └── S1.STL │ │ ├── torso │ │ │ ├── base_link.DAE │ │ │ ├── base_link.STL │ │ │ ├── base_link_collision.DAE │ │ │ └── base_link_collision.STL │ │ ├── upper_elbow │ │ │ ├── E0.DAE │ │ │ └── E0.STL │ │ ├── upper_forearm │ │ │ ├── W0.DAE │ │ │ └── W0.STL │ │ ├── upper_shoulder │ │ │ ├── S0.DAE │ │ │ └── S0.STL │ │ └── wrist │ │ │ ├── W2.DAE │ │ │ └── W2.STL │ │ ├── package.xml │ │ └── urdf │ │ ├── baxter.urdf │ │ ├── baxter.urdf.xacro │ │ ├── baxter_base │ │ ├── baxter_base.gazebo.xacro │ │ └── baxter_base.urdf.xacro │ │ ├── left_end_effector.urdf.xacro │ │ ├── pedestal │ │ └── pedestal.xacro │ │ └── right_end_effector.urdf.xacro └── Panda │ ├── meshes │ ├── collision │ │ ├── finger.obj │ │ ├── hand.obj │ │ ├── link0.obj │ │ ├── link1.obj │ │ ├── link2.obj │ │ ├── link3.obj │ │ ├── link4.obj │ │ ├── link5.obj │ │ ├── link6.obj │ │ └── link7.obj │ └── visual │ │ ├── finger.dae │ │ ├── finger │ │ ├── finger.obj │ │ └── material.lib │ │ ├── hand.dae │ │ ├── hand │ │ ├── hand.obj │ │ └── material.lib │ │ ├── link0.dae │ │ ├── link0 │ │ ├── link0.obj │ │ └── material.lib │ │ ├── link1.dae │ │ ├── link1 │ │ ├── link1.obj │ │ └── material.lib │ │ ├── link2.dae │ │ ├── link2 │ │ ├── link2.obj │ │ └── material.lib │ │ ├── link3.dae │ │ ├── link3 │ │ ├── link3.obj │ │ └── material.lib │ │ ├── link4.dae │ │ ├── link4 │ │ ├── link4.obj │ │ └── material.lib │ │ ├── link5.dae │ │ ├── link5 │ │ ├── link5.obj │ │ └── material.lib │ │ ├── link6.dae │ │ ├── link6 │ │ ├── link6.obj │ │ └── material.lib │ │ ├── link7.dae │ │ └── link7 │ │ ├── link7.obj │ │ └── material.lib │ ├── panda.urdf │ ├── panda_dae.urdf │ └── panda_obj.urdf └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # model weights 132 | weights/ 133 | outputs/ 134 | 135 | # dev codes 136 | dev_* 137 | runs 138 | 139 | # dataset 140 | data/ -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.0.2) 2 | project(ctrnet-robot-pose-estimation-ros) 3 | 4 | ## Compile as C++11, supported in ROS Kinetic and newer 5 | # add_compile_options(-std=c++11) 6 | 7 | ## Find catkin macros and libraries 8 | ## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) 9 | ## is used, also find other catkin packages 10 | find_package(catkin REQUIRED COMPONENTS 11 | roscpp 12 | rospy 13 | std_msgs 14 | ) 15 | 16 | ## System dependencies are found with CMake's conventions 17 | # find_package(Boost REQUIRED COMPONENTS system) 18 | 19 | 20 | ## Uncomment this if the package has a setup.py. This macro ensures 21 | ## modules and global scripts declared therein get installed 22 | ## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html 23 | catkin_python_setup() 24 | 25 | ################################################ 26 | ## Declare ROS messages, services and actions ## 27 | ################################################ 28 | 29 | ## To declare and build messages, services or actions from within this 30 | ## package, follow these steps: 31 | ## * Let MSG_DEP_SET be the set of packages whose message types you use in 32 | ## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). 33 | ## * In the file package.xml: 34 | ## * add a build_depend tag for "message_generation" 35 | ## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET 36 | ## * If MSG_DEP_SET isn't empty the following dependency has been pulled in 37 | ## but can be declared for certainty nonetheless: 38 | ## * add a exec_depend tag for "message_runtime" 39 | ## * In this file (CMakeLists.txt): 40 | ## * add "message_generation" and every package in MSG_DEP_SET to 41 | ## find_package(catkin REQUIRED COMPONENTS ...) 42 | ## * add "message_runtime" and every package in MSG_DEP_SET to 43 | ## catkin_package(CATKIN_DEPENDS ...) 44 | ## * uncomment the add_*_files sections below as needed 45 | ## and list every .msg/.srv/.action file to be processed 46 | ## * uncomment the generate_messages entry below 47 | ## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) 48 | 49 | ## Generate messages in the 'msg' folder 50 | # add_message_files( 51 | # FILES 52 | # Message1.msg 53 | # Message2.msg 54 | # ) 55 | 56 | ## Generate services in the 'srv' folder 57 | # add_service_files( 58 | # FILES 59 | # Service1.srv 60 | # Service2.srv 61 | # ) 62 | 63 | ## Generate actions in the 'action' folder 64 | # add_action_files( 65 | # FILES 66 | # Action1.action 67 | # Action2.action 68 | # ) 69 | 70 | ## Generate added messages and services with any dependencies listed here 71 | # generate_messages( 72 | # DEPENDENCIES 73 | # std_msgs 74 | # ) 75 | 76 | ################################################ 77 | ## Declare ROS dynamic reconfigure parameters ## 78 | ################################################ 79 | 80 | ## To declare and build dynamic reconfigure parameters within this 81 | ## package, follow these steps: 82 | ## * In the file package.xml: 83 | ## * add a build_depend and a exec_depend tag for "dynamic_reconfigure" 84 | ## * In this file (CMakeLists.txt): 85 | ## * add "dynamic_reconfigure" to 86 | ## find_package(catkin REQUIRED COMPONENTS ...) 87 | ## * uncomment the "generate_dynamic_reconfigure_options" section below 88 | ## and list every .cfg file to be processed 89 | 90 | ## Generate dynamic reconfigure parameters in the 'cfg' folder 91 | # generate_dynamic_reconfigure_options( 92 | # cfg/DynReconf1.cfg 93 | # cfg/DynReconf2.cfg 94 | # ) 95 | 96 | ################################### 97 | ## catkin specific configuration ## 98 | ################################### 99 | ## The catkin_package macro generates cmake config files for your package 100 | ## Declare things to be passed to dependent projects 101 | ## INCLUDE_DIRS: uncomment this if your package contains header files 102 | ## LIBRARIES: libraries you create in this project that dependent projects also need 103 | ## CATKIN_DEPENDS: catkin_packages dependent projects also need 104 | ## DEPENDS: system dependencies of this project that dependent projects also need 105 | catkin_package( 106 | # INCLUDE_DIRS include 107 | # LIBRARIES ctrnet-robot-pose-estimation-ros 108 | # CATKIN_DEPENDS roscpp rospy std_msgs 109 | # DEPENDS system_lib 110 | ) 111 | 112 | ########### 113 | ## Build ## 114 | ########### 115 | 116 | ## Specify additional locations of header files 117 | ## Your package locations should be listed before other locations 118 | include_directories( 119 | # include 120 | ${catkin_INCLUDE_DIRS} 121 | ) 122 | 123 | ## Declare a C++ library 124 | # add_library(${PROJECT_NAME} 125 | # src/${PROJECT_NAME}/ctrnet-robot-pose-estimation-ros.cpp 126 | # ) 127 | 128 | ## Add cmake target dependencies of the library 129 | ## as an example, code may need to be generated before libraries 130 | ## either from message generation or dynamic reconfigure 131 | # add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) 132 | 133 | ## Declare a C++ executable 134 | ## With catkin_make all packages are built within a single CMake context 135 | ## The recommended prefix ensures that target names across packages don't collide 136 | # add_executable(${PROJECT_NAME}_node src/ctrnet-robot-pose-estimation-ros_node.cpp) 137 | 138 | ## Rename C++ executable without prefix 139 | ## The above recommended prefix causes long target names, the following renames the 140 | ## target back to the shorter version for ease of user use 141 | ## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" 142 | # set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") 143 | 144 | ## Add cmake target dependencies of the executable 145 | ## same as for the library above 146 | # add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) 147 | 148 | ## Specify libraries to link a library or executable target against 149 | # target_link_libraries(${PROJECT_NAME}_node 150 | # ${catkin_LIBRARIES} 151 | # ) 152 | 153 | ############# 154 | ## Install ## 155 | ############# 156 | 157 | # all install targets should use catkin DESTINATION variables 158 | # See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html 159 | 160 | ## Mark executable scripts (Python etc.) for installation 161 | ## in contrast to setup.py, you can choose the destination 162 | catkin_install_python(PROGRAMS 163 | ros_nodes/panda_pose.py 164 | ros_nodes/baxter_leftarm_pose.py 165 | DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} 166 | ) 167 | 168 | ## Mark executables for installation 169 | ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html 170 | # install(TARGETS ${PROJECT_NAME}_node 171 | # RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} 172 | # ) 173 | 174 | ## Mark libraries for installation 175 | ## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html 176 | # install(TARGETS ${PROJECT_NAME} 177 | # ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} 178 | # LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} 179 | # RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} 180 | # ) 181 | 182 | ## Mark cpp header files for installation 183 | # install(DIRECTORY include/${PROJECT_NAME}/ 184 | # DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} 185 | # FILES_MATCHING PATTERN "*.h" 186 | # PATTERN ".svn" EXCLUDE 187 | # ) 188 | 189 | ## Mark other files for installation (e.g. launch and bag files, etc.) 190 | # install(FILES 191 | # # myfile1 192 | # # myfile2 193 | # DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} 194 | # ) 195 | 196 | ############# 197 | ## Testing ## 198 | ############# 199 | 200 | ## Add gtest based cpp test target and link libraries 201 | # catkin_add_gtest(${PROJECT_NAME}-test test/test_ctrnet-robot-pose-estimation-ros.cpp) 202 | # if(TARGET ${PROJECT_NAME}-test) 203 | # target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) 204 | # endif() 205 | 206 | ## Add folders to be run by python nosetests 207 | # catkin_add_nosetests(test) 208 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # 2 | # ref https://github.com/tebeka/pythonwise/blob/master/docker-miniconda/Dockerfile 3 | # 4 | # miniconda vers: http://repo.continuum.io/miniconda 5 | # sample variations: 6 | # Miniconda3-latest-Linux-armv7l.sh 7 | # Miniconda3-latest-Linux-x86_64.sh 8 | # Miniconda3-py38_4.10.3-Linux-x86_64.sh 9 | # Miniconda3-py37_4.10.3-Linux-x86_64.sh 10 | # 11 | # py vers: https://anaconda.org/anaconda/python/files 12 | # tf vers: https://anaconda.org/anaconda/tensorflow/files 13 | # tf-mkl vers: https://anaconda.org/anaconda/tensorflow-mkl/files 14 | # 15 | 16 | ARG UBUNTU_VER=20.04 17 | ARG CONDA_VER=latest 18 | ARG OS_TYPE=x86_64 19 | ARG PY_VER=3.8.11 20 | 21 | 22 | FROM ubuntu:${UBUNTU_VER} 23 | 24 | # System packages 25 | RUN apt-get -y update 26 | RUN apt-get -y upgrade 27 | RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata 28 | 29 | RUN apt-get install -yq curl build-essential 30 | RUN apt-get install -y git 31 | RUN apt-get install ffmpeg libsm6 libxext6 -y 32 | # Use the above args during building https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact 33 | ARG CONDA_VER 34 | ARG OS_TYPE 35 | # Install miniconda to /miniconda 36 | RUN curl -LO "http://repo.continuum.io/miniconda/Miniconda3-${CONDA_VER}-Linux-${OS_TYPE}.sh" 37 | RUN bash Miniconda3-${CONDA_VER}-Linux-${OS_TYPE}.sh -p /miniconda -b 38 | RUN rm Miniconda3-${CONDA_VER}-Linux-${OS_TYPE}.sh 39 | ENV PATH=/miniconda/bin:${PATH} 40 | RUN conda update -y conda 41 | RUN conda install -c conda-forge jupyterlab 42 | RUN conda install -c conda-forge nb_conda_kernels 43 | 44 | ARG PY_VER 45 | 46 | 47 | RUN mkdir /home/robot_pose_estimation 48 | RUN mkdir /home/dataset 49 | 50 | COPY environment.yml environment.yml 51 | RUN conda env create -f environment.yml 52 | SHELL ["conda","run","-n","pytorch","/bin/bash","-c"] 53 | RUN python -m ipykernel install --name pytorch --display-name "pytorch" 54 | 55 | SHELL ["/bin/bash","-c"] 56 | RUN conda init 57 | RUN echo 'conda activate pytorch' >> ~/.bashrc 58 | 59 | WORKDIR /home 60 | 61 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Jingpei Lu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer 2 | 3 | PyTorch implementation of CtRNet https://arxiv.org/abs/2302.14332 4 | 5 | 6 | ### Dependencies 7 | Recommend set up the environment using Anaconda. 8 | Code is developed and tested on Ubuntu 20.04. 9 | - Python(3.8) 10 | - Numpy(1.22.4) 11 | - PyTorch(1.10.0) 12 | - torchvision(0.11.1) 13 | - pytorch3d(0.6.2) 14 | - Kornia(0.6.3) 15 | - Transforms3d(0.3.1) 16 | 17 | More details see `environment.yml`. 18 | 19 | ## Usage 20 | - See `inference_single_frame.ipynb` for example single frame inference. 21 | - We provide ROS node for CtRNet, which subscribes image and joint state topics and publishes robot pose. 22 | ``` 23 | python ros_node/panda_pose.py 24 | ``` 25 | 26 | ## Dataset 27 | 28 | 1. [DREAM dataset](https://github.com/NVlabs/DREAM/blob/master/data/DOWNLOAD.sh) 29 | 2. [Baxter dataset](https://drive.google.com/file/d/12bCv6GBuh-FdvLGKjlUx2jPN-DBRUqUn/view?usp=share_link) 30 | 31 | ## Weights 32 | 33 | Weights for Panda and Baxter can be found [here](https://drive.google.com/file/d/1OAamxl3_cMLdlpksNo0p-8K20fSMukbI/view?usp=share_link). 34 | 35 | 36 | ## Videos 37 | Using CtRNet for visual servoing experiment with moving camera. 38 | 39 | 40 | 41 | https://user-images.githubusercontent.com/22925036/230662315-5ca62a10-e6b5-4eee-8abc-c3bf2b0fbaae.mp4 42 | 43 | 44 | 45 | https://user-images.githubusercontent.com/22925036/230662322-89b90a32-ca7b-4f64-a6ad-f9b807ddc08d.mp4 46 | 47 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: pytorch_ros 2 | channels: 3 | - pytorch3d 4 | - iopath 5 | - fvcore 6 | - pytorch 7 | - conda-forge 8 | - defaults 9 | dependencies: 10 | - _libgcc_mutex=0.1=main 11 | - _openmp_mutex=4.5=1_gnu 12 | - blas=1.0=mkl 13 | - blosc=1.21.0=h8c45485_0 14 | - brotli=1.0.9=he6710b0_2 15 | - brunsli=0.1=h2531618_0 16 | - bzip2=1.0.8=h7b6447c_0 17 | - c-ares=1.18.1=h7f8727e_0 18 | - ca-certificates=2022.5.18.1=ha878542_0 19 | - cached-property=1.5.2=py_0 20 | - certifi=2022.6.15=py38h06a4308_0 21 | - cfitsio=3.470=hf0d0db6_6 22 | - charls=2.2.0=h2531618_0 23 | - cloudpickle=2.0.0=pyhd3eb1b0_0 24 | - colorama=0.4.4=pyh9f0ad1d_0 25 | - cudatoolkit=11.3.1=h2bc3f7f_2 26 | - cytoolz=0.11.0=py38h7b6447c_0 27 | - dask-core=2022.5.0=py38h06a4308_0 28 | - ffmpeg=4.3=hf484d3e_0 29 | - freetype=2.11.0=h70c0345_0 30 | - fsspec=2022.3.0=py38h06a4308_0 31 | - fvcore=0.1.5.post20210915=py38 32 | - giflib=5.2.1=h7b6447c_0 33 | - gmp=6.2.1=h2531618_2 34 | - gnutls=3.6.15=he1e5248_0 35 | - h5py=3.2.1=py38h6c542dc_0 36 | - hdf5=1.10.6=hb1b8bf9_0 37 | - imagecodecs=2021.8.26=py38h4cda21f_0 38 | - intel-openmp=2021.4.0=h06a4308_3561 39 | - iopath=0.1.9=py38 40 | - jpeg=9d=h7f8727e_0 41 | - jxrlib=1.1=h7b6447c_2 42 | - krb5=1.19.2=hac12032_0 43 | - lame=3.100=h7b6447c_0 44 | - lcms2=2.12=h3be6417_0 45 | - ld_impl_linux-64=2.35.1=h7274673_9 46 | - lerc=3.0=h295c915_0 47 | - libaec=1.0.4=he6710b0_1 48 | - libcurl=7.82.0=h0b77cf5_0 49 | - libdeflate=1.8=h7f8727e_5 50 | - libedit=3.1.20210910=h7f8727e_0 51 | - libev=4.33=h7f8727e_1 52 | - libffi=3.3=he6710b0_2 53 | - libgcc-ng=9.3.0=h5101ec6_17 54 | - libgfortran-ng=7.5.0=ha8ba4b0_17 55 | - libgfortran4=7.5.0=ha8ba4b0_17 56 | - libgomp=9.3.0=h5101ec6_17 57 | - libiconv=1.15=h63c8f33_5 58 | - libidn2=2.3.2=h7f8727e_0 59 | - libnghttp2=1.46.0=hce63b2e_0 60 | - libpng=1.6.37=hbc83047_0 61 | - libssh2=1.10.0=h8f2d780_0 62 | - libstdcxx-ng=9.3.0=hd4cf53a_17 63 | - libtasn1=4.16.0=h27cfd23_0 64 | - libtiff=4.2.0=h85742a9_0 65 | - libunistring=0.9.10=h27cfd23_0 66 | - libuv=1.40.0=h7b6447c_0 67 | - libwebp=1.2.0=h89dd481_0 68 | - libwebp-base=1.2.0=h27cfd23_0 69 | - libzopfli=1.0.3=he6710b0_0 70 | - locket=1.0.0=py38h06a4308_0 71 | - lz4-c=1.9.3=h295c915_1 72 | - mkl=2021.4.0=h06a4308_640 73 | - mkl-service=2.4.0=py38h7f8727e_0 74 | - mkl_fft=1.3.1=py38hd3c417c_0 75 | - mkl_random=1.2.2=py38h51133e4_0 76 | - ncurses=6.3=h7f8727e_0 77 | - nettle=3.7.3=hbbd107a_1 78 | - networkx=2.7.1=pyhd3eb1b0_0 79 | - olefile=0.46=pyhd3eb1b0_0 80 | - openh264=2.1.0=hd408876_0 81 | - openjpeg=2.4.0=h3ad879b_0 82 | - openssl=1.1.1o=h7f8727e_0 83 | - partd=1.2.0=pyhd3eb1b0_1 84 | - pillow=8.4.0=py38h5aabda8_0 85 | - pip=21.2.4=py38h06a4308_0 86 | - portalocker=2.4.0=py38h578d9bd_0 87 | - python=3.8.12=h12debd9_0 88 | - python_abi=3.8=2_cp38 89 | - pytorch=1.10.0=py3.8_cuda11.3_cudnn8.2.0_0 90 | - pytorch-mutex=1.0=cuda 91 | - pytorch3d=0.6.2=py38_cu113_pyt1100 92 | - pywavelets=1.3.0=py38h7f8727e_0 93 | - readline=8.1=h27cfd23_0 94 | - scikit-image=0.19.2=py38h51133e4_0 95 | - scipy=1.7.3=py38hc147768_0 96 | - setuptools=58.0.4=py38h06a4308_0 97 | - six=1.16.0=pyhd3eb1b0_0 98 | - snappy=1.1.9=h295c915_0 99 | - sqlite=3.36.0=hc218d9a_0 100 | - tabulate=0.8.9=pyhd8ed1ab_0 101 | - termcolor=1.1.0=py_2 102 | - tifffile=2021.7.2=pyhd3eb1b0_2 103 | - tk=8.6.11=h1ccaba5_0 104 | - toolz=0.11.2=pyhd3eb1b0_0 105 | - torchaudio=0.10.0=py38_cu113 106 | - torchvision=0.11.1=py38_cu113 107 | - tqdm=4.64.0=pyhd8ed1ab_0 108 | - typing_extensions=3.10.0.2=pyh06a4308_0 109 | - wheel=0.37.0=pyhd3eb1b0_1 110 | - xz=5.2.5=h7b6447c_0 111 | - yacs=0.1.8=pyhd8ed1ab_0 112 | - yaml=0.2.5=h7b6447c_0 113 | - zfp=0.5.5=h295c915_6 114 | - zlib=1.2.11=h7b6447c_3 115 | - zstd=1.4.9=haebb681_0 116 | - pip: 117 | - absl-py==1.1.0 118 | - ansitable==0.9.6 119 | - anyio==3.3.4 120 | - argon2-cffi==21.1.0 121 | - attrdict==2.0.1 122 | - attrs==21.2.0 123 | - av==9.2.0 124 | - babel==2.9.1 125 | - backcall==0.2.0 126 | - bleach==4.1.0 127 | - cachetools==5.2.0 128 | - catkin-pkg==0.5.2 129 | - cffi==1.15.0 130 | - charset-normalizer==2.0.7 131 | - colored==1.4.3 132 | - debugpy==1.5.1 133 | - decorator==5.1.0 134 | - defusedxml==0.7.1 135 | - distro==1.8.0 136 | - docutils==0.19 137 | - entrypoints==0.3 138 | - gnupg==2.3.1 139 | - google-auth==2.8.0 140 | - google-auth-oauthlib==0.4.6 141 | - grpcio==1.47.0 142 | - idna==3.3 143 | - imageio==2.10.1 144 | - importlib-metadata==4.11.4 145 | - imutils==0.5.4 146 | - ipykernel==6.5.0 147 | - ipython==7.29.0 148 | - ipython-genutils==0.2.0 149 | - jedi==0.18.0 150 | - jinja2==3.0.2 151 | - json5==0.9.6 152 | - jsonschema==4.1.2 153 | - jupyter-client==7.0.6 154 | - jupyter-core==4.9.1 155 | - jupyter-server==1.11.2 156 | - jupyterlab==3.2.1 157 | - jupyterlab-pygments==0.1.2 158 | - jupyterlab-server==2.8.2 159 | - kornia==0.6.3 160 | - markdown==3.3.7 161 | - markupsafe==2.0.1 162 | - matlabengineforpython==R2021a 163 | - matplotlib-inline==0.1.3 164 | - mistune==0.8.4 165 | - nbclassic==0.3.4 166 | - nbclient==0.5.4 167 | - nbconvert==6.2.0 168 | - nbformat==5.1.3 169 | - nest-asyncio==1.5.1 170 | - notebook==6.4.5 171 | - numpy==1.22.4 172 | - oauthlib==3.2.0 173 | - opencv-contrib-python==4.5.4.58 174 | - packaging==21.2 175 | - pandocfilters==1.5.0 176 | - parso==0.8.2 177 | - pexpect==4.8.0 178 | - pgraph-python==0.6.1 179 | - pickleshare==0.7.5 180 | - progress==1.6 181 | - progressbar==2.5 182 | - prometheus-client==0.12.0 183 | - prompt-toolkit==3.0.21 184 | - protobuf==3.19.4 185 | - psutil==5.9.3 186 | - ptyprocess==0.7.0 187 | - pyasn1==0.4.8 188 | - pyasn1-modules==0.2.8 189 | - pycparser==2.20 190 | - pycryptodomex==3.15.0 191 | - pygments==2.10.0 192 | - pymunk==6.2.1 193 | - pyparsing==2.4.7 194 | - pyrsistent==0.18.0 195 | - python-dateutil==2.8.2 196 | - pytz==2021.3 197 | - pyyaml==6.0 198 | - pyzmq==22.3.0 199 | - requests==2.26.0 200 | - requests-oauthlib==1.3.1 201 | - roboticstoolbox-python==1.0.1 202 | - rospkg==1.4.0 203 | - rsa==4.8 204 | - rtb-data==1.0.0 205 | - ruamel-yaml==0.17.21 206 | - ruamel-yaml-clib==0.2.6 207 | - scikit-fmm==2022.3.26 208 | - send2trash==1.8.0 209 | - sniffio==1.2.0 210 | - spatialgeometry==1.0.1 211 | - spatialmath-python==1.0.0 212 | - swift-sim==1.0.0 213 | - tensorboard==2.9.1 214 | - tensorboard-data-server==0.6.1 215 | - tensorboard-plugin-wit==1.8.1 216 | - terminado==0.12.1 217 | - testpath==0.5.0 218 | - tornado==6.1 219 | - traitlets==5.1.1 220 | - transforms3d==0.3.1 221 | - urllib3==1.26.7 222 | - wcwidth==0.2.5 223 | - webencodings==0.5.1 224 | - websocket-client==1.2.1 225 | - websockets==10.3 226 | - werkzeug==2.1.2 227 | - zipp==3.8.0 228 | prefix: /home/jingpei/anaconda3/envs/pytorch_ros 229 | -------------------------------------------------------------------------------- /evaluation/evaluate_DREAM.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import sys\n", 10 | "import os\n", 11 | "base_dir = \"/home/jingpei/Desktop/CtRNet-robot-pose-estimation\"\n", 12 | "sys.path.append(base_dir)\n", 13 | "\n", 14 | "import torch\n", 15 | "from torch.utils.data import Dataset, DataLoader\n", 16 | "import torchvision.transforms as transforms\n", 17 | "import numpy as np\n", 18 | "import matplotlib.pyplot as plt\n", 19 | "\n", 20 | "from utils import *\n", 21 | "from models.CtRNet import CtRNet\n", 22 | "from models.BPnP import batch_transform_3d\n", 23 | "\n" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 2, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "import argparse\n", 33 | "from imageloaders.DREAM import ImageDataLoaderReal, load_camera_parameters\n", 34 | "parser = argparse.ArgumentParser()\n", 35 | "\n", 36 | "args = parser.parse_args(\"\")\n", 37 | "\n", 38 | "args.data_folder = \"/media/jingpei/DATA/DREAM/data/real/panda-3cam_realsense\"\n", 39 | "args.base_dir = \"/home/jingpei/Desktop/CtRNet-robot-pose-estimation\"\n", 40 | "args.use_gpu = True\n", 41 | "args.trained_on_multi_gpus = True\n", 42 | "args.keypoint_seg_model_path = os.path.join(args.base_dir,\"weights/panda/panda-3cam_realsense/net.pth\")\n", 43 | "args.urdf_file = os.path.join(args.base_dir,\"urdfs/Panda/panda.urdf\")\n", 44 | "\n", 45 | "args.robot_name = 'Panda' # \"Panda\" or \"Baxter_left_arm\"\n", 46 | "args.n_kp = 7\n", 47 | "args.scale = 0.5\n", 48 | "args.height = 480\n", 49 | "args.width = 640\n", 50 | "args.fx, args.fy, args.px, args.py = load_camera_parameters(args.data_folder)\n", 51 | "\n", 52 | "# scale the camera parameters\n", 53 | "args.width = int(args.width * args.scale)\n", 54 | "args.height = int(args.height * args.scale)\n", 55 | "args.fx = args.fx * args.scale\n", 56 | "args.fy = args.fy * args.scale\n", 57 | "args.px = args.px * args.scale\n", 58 | "args.py = args.py * args.scale" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 3, 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "Loading keypoint segmentation model from /home/jingpei/Desktop/CtRNet-robot-pose-estimation/weights/panda/panda-3cam_realsense/net.pth\n", 71 | "Camera intrinsics: [[307.76196289 0. 164.13032532]\n", 72 | " [ 0. 307.60958862 125.89585114]\n", 73 | " [ 0. 0. 1. ]]\n", 74 | "Robot model: Panda\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "CtRNet = CtRNet(args)" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 4, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "trans_to_tensor = transforms.Compose([\n", 89 | " transforms.ToTensor(),\n", 90 | " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n", 91 | "])\n", 92 | "\n", 93 | "dataset = ImageDataLoaderReal(data_folder = args.data_folder, scale = args.scale, trans_to_tensor = trans_to_tensor)" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 5, 99 | "metadata": {}, 100 | "outputs": [ 101 | { 102 | "data": { 103 | "application/vnd.jupyter.widget-view+json": { 104 | "model_id": "9de685e4b97242bfa46d52994ff1ef06", 105 | "version_major": 2, 106 | "version_minor": 0 107 | }, 108 | "text/plain": [ 109 | " 0%| | 0/5944 [00:00 0: 74 | pts2d_flat.grad.zero_() 75 | P_6d_flat.grad.zero_() 76 | pts3d_flat.grad.zero_() 77 | K_flat.grad.zero_() 78 | 79 | R = kn.geometry.conversions.angle_axis_to_rotation_matrix(P_6d_flat[0:m-3].view(1,3)) 80 | 81 | P = torch.cat((R[0,0:3,0:3].view(3,3), P_6d_flat[m-3:m].view(3,1)),dim=-1) 82 | KP = torch.mm(K_flat.view(3,3), P) 83 | pts2d_i = pts2d_flat.view(n,2).transpose(0,1) 84 | pts3d_i = torch.cat((pts3d_flat.view(n,3),torch.ones(n,1,device=device)),dim=-1).t() 85 | proj_i = KP.mm(pts3d_i) 86 | Si = proj_i[2,:].view(1,n) 87 | 88 | r = pts2d_i*Si-proj_i[0:2,:] 89 | coefs = get_coefs(P_6d_flat.view(1,6), pts3d_flat.view(n,3), K_flat.view(3,3)) 90 | coef = coefs[:,:,j].transpose(0,1) # size: [2,n] 91 | fj = (coef*r).sum() 92 | fj.backward() 93 | J_fy[j,:] = P_6d_flat.grad.clone() 94 | J_fx[j,:] = pts2d_flat.grad.clone() 95 | J_fz[j,:] = pts3d_flat.grad.clone() 96 | J_fK[j,:] = K_flat.grad.clone() 97 | 98 | inv_J_fy = torch.inverse(J_fy) 99 | 100 | J_yx = (-1) * torch.mm(inv_J_fy, J_fx) 101 | J_yz = (-1) * torch.mm(inv_J_fy, J_fz) 102 | J_yK = (-1) * torch.mm(inv_J_fy, J_fK) 103 | 104 | grad_x[i] = grad_output[i].view(1,m).mm(J_yx).view(n,2) 105 | grad_z += grad_output[i].view(1,m).mm(J_yz).view(n,3) 106 | grad_K += grad_output[i].view(1,m).mm(J_yK).view(3,3) 107 | 108 | return grad_x, grad_z, grad_K, None 109 | 110 | 111 | class BPnP_m3d(torch.autograd.Function): 112 | """ 113 | BPnP_m3d supports mini-batch intputs of 3D keypoints, where the i-th set of 2D keypoints correspond to the i-th set of 3D keypoints. 114 | INPUTS: 115 | pts2d - the 2D keypoints coordinates of size [batch_size, num_keypoints, 2] 116 | pts3d - the 3D keypoints coordinates of size [batch_size, num_keypoints, 3] 117 | K - the camera intrinsic matrix of size [3, 3] 118 | OUTPUT: 119 | P_6d - the 6 DOF poses of size [batch_size, 6], where the first 3 elements of each row are the angle-axis rotation 120 | vector (Euler vector) and the last 3 elements are the translation vector. 121 | NOTE: 122 | For situations where all sets of 2D points in the mini-batch correspond to one common set of 3D points, use the BPnP class. 123 | """ 124 | @staticmethod 125 | def forward(ctx, pts2d, pts3d, K, ini_pose=None): 126 | bs = pts2d.size(0) 127 | n = pts2d.size(1) 128 | device = pts2d.device 129 | K_np = np.array(K.detach().cpu()) 130 | P_6d = torch.zeros(bs,6,device=device) 131 | 132 | for i in range(bs): 133 | pts2d_i_np = np.ascontiguousarray(pts2d[i].detach().cpu()).reshape((n,1,2)) 134 | pts3d_i_np = np.ascontiguousarray(pts3d[i].detach().cpu()).reshape((n,3)) 135 | if ini_pose is None: 136 | #_, rvec0, T0, _ = cv.solvePnPRansac(objectPoints=pts3d_i_np, imagePoints=pts2d_i_np, cameraMatrix=K_np, distCoeffs=None, flags=cv.SOLVEPNP_ITERATIVE, confidence=0.9999 ,reprojectionError=1) 137 | _, rvec0, T0 = cv.solvePnP(objectPoints=pts3d_i_np, imagePoints=pts2d_i_np, cameraMatrix=K_np, distCoeffs=None, flags=cv.SOLVEPNP_EPNP) 138 | else: 139 | rvec0 = np.array(ini_pose[i, 0:3].cpu().view(3, 1)) 140 | T0 = np.array(ini_pose[i, 3:6].cpu().view(3, 1)) 141 | _, rvec, T = cv.solvePnP(objectPoints=pts3d_i_np, imagePoints=pts2d_i_np, cameraMatrix=K_np, distCoeffs=None, flags=cv.SOLVEPNP_ITERATIVE, useExtrinsicGuess=True, rvec=rvec0, tvec=T0) 142 | angle_axis = torch.tensor(rvec,device=device,dtype=torch.float).view(1, 3) 143 | T = torch.tensor(T,device=device,dtype=torch.float).view(1, 3) 144 | P_6d[i,:] = torch.cat((angle_axis,T),dim=-1) 145 | 146 | ctx.save_for_backward(pts2d,P_6d,pts3d,K) 147 | return P_6d 148 | 149 | @staticmethod 150 | def backward(ctx, grad_output): 151 | 152 | pts2d, P_6d, pts3d, K = ctx.saved_tensors 153 | device = pts2d.device 154 | bs = pts2d.size(0) 155 | n = pts2d.size(1) 156 | m = 6 157 | 158 | grad_x = torch.zeros_like(pts2d) 159 | grad_z = torch.zeros_like(pts3d) 160 | grad_K = torch.zeros_like(K) 161 | 162 | for i in range(bs): 163 | J_fy = torch.zeros(m,m, device=device) 164 | J_fx = torch.zeros(m,2*n, device=device) 165 | J_fz = torch.zeros(m,3*n, device=device) 166 | J_fK = torch.zeros(m, 9, device=device) 167 | 168 | torch.set_grad_enabled(True) 169 | pts2d_flat = pts2d[i].clone().view(-1).detach().requires_grad_() 170 | P_6d_flat = P_6d[i].clone().view(-1).detach().requires_grad_() 171 | pts3d_flat = pts3d[i].clone().view(-1).detach().requires_grad_() 172 | K_flat = K.clone().view(-1).detach().requires_grad_() 173 | 174 | for j in range(m): 175 | torch.set_grad_enabled(True) 176 | if j > 0: 177 | pts2d_flat.grad.zero_() 178 | P_6d_flat.grad.zero_() 179 | pts3d_flat.grad.zero_() 180 | K_flat.grad.zero_() 181 | 182 | R = kn.geometry.conversions.angle_axis_to_rotation_matrix(P_6d_flat[0:m-3].view(1,3)) 183 | 184 | P = torch.cat((R[0,0:3,0:3].view(3,3), P_6d_flat[m-3:m].view(3,1)),dim=-1) 185 | KP = torch.mm(K_flat.view(3,3), P) 186 | pts2d_i = pts2d_flat.view(n,2).transpose(0,1) 187 | pts3d_i = torch.cat((pts3d_flat.view(n,3),torch.ones(n,1,device=device)),dim=-1).t() 188 | proj_i = KP.mm(pts3d_i) 189 | Si = proj_i[2,:].view(1,n) 190 | 191 | r = pts2d_i*Si-proj_i[0:2,:] 192 | coefs = get_coefs(P_6d_flat.view(1,6), pts3d_flat.view(n,3), K_flat.view(3,3)) 193 | coef = coefs[:,:,j].transpose(0,1) # size: [2,n] 194 | fj = (coef*r).sum() 195 | fj.backward() 196 | J_fy[j,:] = P_6d_flat.grad.clone() 197 | J_fx[j,:] = pts2d_flat.grad.clone() 198 | J_fz[j,:] = pts3d_flat.grad.clone() 199 | J_fK[j,:] = K_flat.grad.clone() 200 | 201 | inv_J_fy = torch.inverse(J_fy) 202 | 203 | J_yx = (-1) * torch.mm(inv_J_fy, J_fx) 204 | J_yz = (-1) * torch.mm(inv_J_fy, J_fz) 205 | J_yK = (-1) * torch.mm(inv_J_fy, J_fK) 206 | 207 | grad_x[i] = grad_output[i].view(1,m).mm(J_yx).view(n,2) 208 | grad_z[i] = grad_output[i].view(1,m).mm(J_yz).view(n,3) 209 | grad_K += grad_output[i].view(1,m).mm(J_yK).view(3,3) 210 | 211 | return grad_x, grad_z, grad_K, None 212 | 213 | 214 | class BPnP_fast(torch.autograd.Function): 215 | """ 216 | BPnP_fast is the efficient version of the BPnP class which ignores the higher order dirivatives through the coefs' graph. This sacrifices 217 | negligible gradient accuracy yet saves significant runtime. 218 | INPUTS: 219 | pts2d - the 2D keypoints coordinates of size [batch_size, num_keypoints, 2] 220 | pts3d - the 3D keypoints coordinates of size [num_keypoints, 3] 221 | K - the camera intrinsic matrix of size [3, 3] 222 | OUTPUT: 223 | P_6d - the 6 DOF poses of size [batch_size, 6], where the first 3 elements of each row are the angle-axis rotation 224 | vector (Euler vector) and the last 3 elements are the translation vector. 225 | NOTE: 226 | This BPnP function assumes that all sets of 2D points in the mini-batch correspond to one common set of 3D points. 227 | For situations where pts3d is also a mini-batch, use the BPnP_m3d class. 228 | """ 229 | @staticmethod 230 | def forward(ctx, pts2d, pts3d, K, ini_pose=None): 231 | bs = pts2d.size(0) 232 | n = pts2d.size(1) 233 | device = pts2d.device 234 | pts3d_np = np.array(pts3d.detach().cpu()) 235 | K_np = np.array(K.detach().cpu()) 236 | P_6d = torch.zeros(bs,6,device=device) 237 | 238 | for i in range(bs): 239 | pts2d_i_np = np.ascontiguousarray(pts2d[i].detach().cpu()).reshape((n,1,2)) 240 | if ini_pose is None: 241 | _, rvec0, T0, _ = cv.solvePnPRansac(objectPoints=pts3d_np, imagePoints=pts2d_i_np, cameraMatrix=K_np, distCoeffs=None, flags=cv.SOLVEPNP_ITERATIVE, confidence=0.9999 ,reprojectionError=3) 242 | else: 243 | rvec0 = np.array(ini_pose[i, 0:3].cpu().view(3, 1)) 244 | T0 = np.array(ini_pose[i, 3:6].cpu().view(3, 1)) 245 | _, rvec, T = cv.solvePnP(objectPoints=pts3d_np, imagePoints=pts2d_i_np, cameraMatrix=K_np, distCoeffs=None, flags=cv.SOLVEPNP_ITERATIVE, useExtrinsicGuess=True, rvec=rvec0, tvec=T0) 246 | angle_axis = torch.tensor(rvec,device=device,dtype=torch.float).view(1, 3) 247 | T = torch.tensor(T,device=device,dtype=torch.float).view(1, 3) 248 | P_6d[i,:] = torch.cat((angle_axis,T),dim=-1) 249 | 250 | ctx.save_for_backward(pts2d,P_6d,pts3d,K) 251 | return P_6d 252 | 253 | @staticmethod 254 | def backward(ctx, grad_output): 255 | 256 | pts2d, P_6d, pts3d, K = ctx.saved_tensors 257 | device = pts2d.device 258 | bs = pts2d.size(0) 259 | n = pts2d.size(1) 260 | m = 6 261 | 262 | grad_x = torch.zeros_like(pts2d) 263 | grad_z = torch.zeros_like(pts3d) 264 | grad_K = torch.zeros_like(K) 265 | 266 | for i in range(bs): 267 | J_fy = torch.zeros(m,m, device=device) 268 | J_fx = torch.zeros(m,2*n, device=device) 269 | J_fz = torch.zeros(m,3*n, device=device) 270 | J_fK = torch.zeros(m, 9, device=device) 271 | 272 | coefs = get_coefs(P_6d[i].view(1,6), pts3d, K, create_graph=False).detach() 273 | 274 | pts2d_flat = pts2d[i].clone().view(-1).detach().requires_grad_() 275 | P_6d_flat = P_6d[i].clone().view(-1).detach().requires_grad_() 276 | pts3d_flat = pts3d.clone().view(-1).detach().requires_grad_() 277 | K_flat = K.clone().view(-1).detach().requires_grad_() 278 | 279 | for j in range(m): 280 | torch.set_grad_enabled(True) 281 | if j > 0: 282 | pts2d_flat.grad.zero_() 283 | P_6d_flat.grad.zero_() 284 | pts3d_flat.grad.zero_() 285 | K_flat.grad.zero_() 286 | 287 | R = kn.geometry.conversions.angle_axis_to_rotation_matrix(P_6d_flat[0:m-3].view(1,3)) 288 | 289 | P = torch.cat((R[0,0:3,0:3].view(3,3), P_6d_flat[m-3:m].view(3,1)),dim=-1) 290 | KP = torch.mm(K_flat.view(3,3), P) 291 | pts2d_i = pts2d_flat.view(n,2).transpose(0,1) 292 | pts3d_i = torch.cat((pts3d_flat.view(n,3),torch.ones(n,1,device=device)),dim=-1).t() 293 | proj_i = KP.mm(pts3d_i) 294 | Si = proj_i[2,:].view(1,n) 295 | 296 | r = pts2d_i*Si-proj_i[0:2,:] 297 | coef = coefs[:,:,j].transpose(0,1) # size: [2,n] 298 | fj = (coef*r).sum() 299 | fj.backward() 300 | J_fy[j,:] = P_6d_flat.grad.clone() 301 | J_fx[j,:] = pts2d_flat.grad.clone() 302 | J_fz[j,:] = pts3d_flat.grad.clone() 303 | J_fK[j,:] = K_flat.grad.clone() 304 | 305 | inv_J_fy = torch.inverse(J_fy) 306 | 307 | J_yx = (-1) * torch.mm(inv_J_fy, J_fx) 308 | J_yz = (-1) * torch.mm(inv_J_fy, J_fz) 309 | J_yK = (-1) * torch.mm(inv_J_fy, J_fK) 310 | 311 | grad_x[i] = grad_output[i].view(1,m).mm(J_yx).view(n,2) 312 | grad_z += grad_output[i].view(1,m).mm(J_yz).view(n,3) 313 | grad_K += grad_output[i].view(1,m).mm(J_yK).view(3,3) 314 | 315 | return grad_x, grad_z, grad_K, None 316 | 317 | 318 | def get_coefs(P_6d, pts3d, K, create_graph=True): 319 | device = P_6d.device 320 | n = pts3d.size(0) 321 | m = P_6d.size(-1) 322 | coefs = torch.zeros(n,2,m,device=device) 323 | torch.set_grad_enabled(True) 324 | y = P_6d.repeat(n,1) 325 | proj = batch_project(y, pts3d, K).squeeze() 326 | vec = torch.diag(torch.ones(n,device=device).float()) 327 | for k in range(2): 328 | torch.set_grad_enabled(True) 329 | y_grad = torch.autograd.grad(proj[:,:,k],y,vec, retain_graph=True, create_graph=create_graph) 330 | coefs[:,k,:] = -2*y_grad[0].clone() 331 | return coefs 332 | 333 | def batch_project(P, pts3d, K, angle_axis=True): 334 | # pts3d.size = (N,3) 335 | # P.size = (B,6) 336 | n = pts3d.size(0) 337 | bs = P.size(0) 338 | device = P.device 339 | pts3d_h = torch.cat((pts3d, torch.ones(n, 1, device=device)), dim=-1) 340 | if angle_axis: 341 | R_out = kn.geometry.conversions.angle_axis_to_rotation_matrix(P[:, 0:3].view(bs, 3)) 342 | PM = torch.cat((R_out[:,0:3,0:3], P[:, 3:6].view(bs, 3, 1)), dim=-1) 343 | else: 344 | PM = P 345 | pts3d_cam = pts3d_h.matmul(PM.transpose(-2,-1)) 346 | pts2d_proj = pts3d_cam.matmul(K.t()) 347 | S = pts2d_proj[:,:, 2].view(bs, n, 1) 348 | pts2d_pro = pts2d_proj[:,:,0:2].div(S) 349 | 350 | return pts2d_pro 351 | 352 | def batch_transform_3d(P, pts3d, angle_axis=True): 353 | # pts3d.size = (N,3) 354 | # P.size = (B,6) 355 | n = pts3d.size(0) 356 | bs = P.size(0) 357 | device = P.device 358 | pts3d_h = torch.cat((pts3d, torch.ones(n, 1, device=device)), dim=-1) 359 | if angle_axis: 360 | R_out = kn.geometry.conversions.angle_axis_to_rotation_matrix(P[:, 0:3].view(bs, 3)) 361 | PM = torch.cat((R_out[:,0:3,0:3], P[:, 3:6].view(bs, 3, 1)), dim=-1) 362 | else: 363 | PM = P 364 | pts3d_cam = pts3d_h.matmul(PM.transpose(-2,-1)) 365 | 366 | return pts3d_cam 367 | 368 | 369 | 370 | 371 | -------------------------------------------------------------------------------- /models/CtRNet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | import kornia 4 | import numpy as np 5 | 6 | from .keypoint_seg_resnet import KeyPointSegNet 7 | from .BPnP import BPnP, BPnP_m3d, batch_project 8 | from .mesh_renderer import RobotMeshRenderer 9 | 10 | 11 | class CtRNet(torch.nn.Module): 12 | def __init__(self, args): 13 | super(CtRNet, self).__init__() 14 | 15 | self.args = args 16 | 17 | if args.use_gpu: 18 | self.device = "cuda" 19 | else: 20 | self.device = "cpu" 21 | 22 | # load keypoint segmentation model 23 | self.keypoint_seg_predictor = KeyPointSegNet(args, use_gpu=args.use_gpu) 24 | 25 | if args.use_gpu: 26 | self.keypoint_seg_predictor = self.keypoint_seg_predictor.cuda() 27 | 28 | if args.trained_on_multi_gpus == True: 29 | self.keypoint_seg_predictor = torch.nn.DataParallel(self.keypoint_seg_predictor, device_ids=[0]) 30 | 31 | if args.keypoint_seg_model_path is not None: 32 | print("Loading keypoint segmentation model from {}".format(args.keypoint_seg_model_path)) 33 | self.keypoint_seg_predictor.load_state_dict(torch.load(args.keypoint_seg_model_path)) 34 | 35 | self.keypoint_seg_predictor.eval() 36 | 37 | # load BPnP 38 | self.bpnp = BPnP.apply 39 | self.bpnp_m3d = BPnP_m3d.apply 40 | 41 | # set up camera intrinsics 42 | 43 | self.intrinsics = np.array([[ args.fx, 0. , args.px ], 44 | [ 0. , args.fy, args.py ], 45 | [ 0. , 0. , 1. ]]) 46 | print("Camera intrinsics: {}".format(self.intrinsics)) 47 | 48 | self.K = torch.tensor(self.intrinsics, device=self.device, dtype=torch.float) 49 | 50 | 51 | # set up robot model 52 | if args.robot_name == "Panda": 53 | from .robot_arm import PandaArm 54 | self.robot = PandaArm(args.urdf_file) 55 | elif args.robot_name == "Baxter_left_arm": 56 | from .robot_arm import BaxterLeftArm 57 | self.robot = BaxterLeftArm(args.urdf_file) 58 | print("Robot model: {}".format(args.robot_name)) 59 | 60 | 61 | def inference_single_image(self, img, joint_angles): 62 | # img: (3, H, W) 63 | # joint_angles: (7) 64 | # robot: robot model 65 | 66 | # detect 2d keypoints and segmentation masks 67 | points_2d, segmentation = self.keypoint_seg_predictor(img[None]) 68 | foreground_mask = torch.sigmoid(segmentation) 69 | _,t_list = self.robot.get_joint_RT(joint_angles) 70 | points_3d = torch.from_numpy(np.array(t_list)).float().to(self.device) 71 | if self.args.robot_name == "Panda": 72 | points_3d = points_3d[[0,2,3,4,6,7,8]] # remove 1 and 5 links as they are overlapping with 2 and 6 73 | 74 | #init_pose = torch.tensor([[ 1.5497, 0.5420, -0.3909, -0.4698, -0.0211, 1.3243]]) 75 | #cTr = bpnp(points_2d_pred, points_3d, K, init_pose) 76 | cTr = self.bpnp(points_2d, points_3d, self.K) 77 | 78 | return cTr, points_2d, foreground_mask 79 | 80 | def inference_batch_images(self, img, joint_angles): 81 | # img: (B, 3, H, W) 82 | # joint_angles: (B, 7) 83 | # robot: robot model 84 | 85 | # detect 2d keypoints and segmentation masks 86 | points_2d, segmentation = self.keypoint_seg_predictor(img) 87 | foreground_mask = torch.sigmoid(segmentation) 88 | 89 | points_3d_batch = [] 90 | for b in range(joint_angles.shape[0]): 91 | _,t_list = self.robot.get_joint_RT(joint_angles[b]) 92 | points_3d = torch.from_numpy(np.array(t_list)).float().to(self.device) 93 | if self.args.robot_name == "Panda": 94 | points_3d = points_3d[:,[0,2,3,4,6,7,8]] 95 | points_3d_batch.append(points_3d[None]) 96 | 97 | points_3d_batch = torch.cat(points_3d_batch, dim=0) 98 | 99 | cTr = self.bpnp_m3d(points_2d, points_3d_batch, self.K) 100 | 101 | return cTr, points_2d, foreground_mask 102 | 103 | 104 | def cTr_to_pose_matrix(self, cTr): 105 | """ 106 | cTr: (batch_size, 6) 107 | pose_matrix: (batch_size, 4, 4) 108 | """ 109 | batch_size = cTr.shape[0] 110 | pose_matrix = torch.zeros((batch_size, 4, 4), device=self.device) 111 | pose_matrix[:, :3, :3] = kornia.geometry.conversions.angle_axis_to_rotation_matrix(cTr[:, :3]) 112 | pose_matrix[:, :3, 3] = cTr[:, 3:] 113 | pose_matrix[:, 3, 3] = 1 114 | return pose_matrix 115 | 116 | def to_valid_R_batch(self, R): 117 | # R is a batch of 3x3 rotation matrices 118 | U, S, V = torch.svd(R) 119 | return torch.bmm(U, V.transpose(1,2)) 120 | 121 | def setup_robot_renderer(self, mesh_files): 122 | # mesh_files: list of mesh files 123 | focal_length = [-self.args.fx,-self.args.fy] 124 | principal_point = [self.args.px, self.args.py] 125 | image_size = [self.args.height,self.args.width] 126 | 127 | robot_renderer = RobotMeshRenderer( 128 | focal_length=focal_length, principal_point=principal_point, image_size=image_size, 129 | robot=self.robot, mesh_files=mesh_files, device=self.device) 130 | 131 | return robot_renderer 132 | 133 | def render_single_robot_mask(self, cTr, robot_mesh, robot_renderer): 134 | # cTr: (6) 135 | # img: (1, H, W) 136 | 137 | R = kornia.geometry.conversions.angle_axis_to_rotation_matrix(cTr[:3][None]) # (1, 3, 3) 138 | R = torch.transpose(R,1,2) 139 | #R = to_valid_R_batch(R) 140 | T = cTr[3:][None] # (1, 3) 141 | 142 | if T[0,-1] < 0: 143 | rendered_image = robot_renderer.silhouette_renderer(meshes_world=robot_mesh, R = -R, T = -T) 144 | else: 145 | rendered_image = robot_renderer.silhouette_renderer(meshes_world=robot_mesh, R = R, T = T) 146 | 147 | if torch.isnan(rendered_image).any(): 148 | rendered_image = torch.nan_to_num(rendered_image) 149 | 150 | return rendered_image[..., 3] 151 | 152 | 153 | 154 | def train_on_batch(self, img, joint_angles, robot_renderer, criterions, phase='train'): 155 | # img: (B, 3, H, W) 156 | # joint_angles: (B, 7) 157 | with torch.set_grad_enabled(phase == 'train'): 158 | # detect 2d keypoints 159 | points_2d, segmentation = self.keypoint_seg_predictor(img) 160 | 161 | mask_list = list() 162 | seg_weight_list = list() 163 | 164 | for b in range(img.shape[0]): 165 | # get 3d points 166 | _,t_list = self.robot.get_joint_RT(joint_angles[b]) 167 | points_3d = torch.from_numpy(np.array(t_list)).float().to(self.device) 168 | if self.args.robot_name == "Panda": 169 | points_3d = points_3d[:,[0,2,3,4,6,7,8]] 170 | 171 | # get camera pose 172 | cTr = self.bpnp(points_2d[b][None], points_3d, self.K) 173 | 174 | # config robot mesh 175 | robot_mesh = robot_renderer.get_robot_mesh(joint_angles[b]) 176 | 177 | # render robot mask 178 | rendered_image = self.render_single_robot_mask(cTr.squeeze(), robot_mesh, robot_renderer) 179 | 180 | mask_list.append(rendered_image) 181 | points_2d_proj = batch_project(cTr, points_3d, self.K) 182 | reproject_error = criterions["mse_mean"](points_2d[b], points_2d_proj.squeeze()) 183 | seg_weight = torch.exp(-reproject_error * self.args.reproj_err_scale) 184 | seg_weight_list.append(seg_weight) 185 | 186 | mask_batch = torch.cat(mask_list,0) 187 | 188 | loss_bce = 0 189 | for b in range(segmentation.shape[0]): 190 | loss_bce = loss_bce + seg_weight_list[b] * criterions["bce"](segmentation[b].squeeze(), mask_batch[b].detach()) 191 | 192 | img_ref = torch.sigmoid(segmentation).detach() 193 | #loss_reproj = 0.0005 * criterionMSE_mean(points_2d, points_2d_proj_batch) 194 | loss_mse = 0.001 * criterions["mse_sum"](mask_batch, img_ref.squeeze()) 195 | loss = loss_mse + loss_bce 196 | 197 | return loss 198 | 199 | 200 | 201 | 202 | 203 | -------------------------------------------------------------------------------- /models/keypoint_seg_resnet.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torch.nn.functional as F 4 | from torch.autograd import Variable 5 | import numpy as np 6 | import torchvision.models as models 7 | 8 | 9 | 10 | class KeypointUpSample(nn.Module): 11 | def __init__(self, in_channels, num_keypoints): 12 | super().__init__() 13 | input_features = in_channels 14 | deconv_kernel = 4 15 | self.kps_score_lowres = nn.ConvTranspose2d( 16 | input_features, 17 | num_keypoints, 18 | deconv_kernel, 19 | stride=2, 20 | padding=deconv_kernel // 2 - 1, 21 | ) 22 | nn.init.kaiming_normal_(self.kps_score_lowres.weight, mode="fan_out", nonlinearity="relu") 23 | nn.init.constant_(self.kps_score_lowres.bias, 0) 24 | #nn.init.uniform_(self.kps_score_lowres.weight) 25 | #nn.init.uniform_(self.kps_score_lowres.bias) 26 | self.up_scale = 1 27 | self.out_channels = num_keypoints 28 | 29 | def forward(self, x): 30 | x = self.kps_score_lowres(x) 31 | return torch.nn.functional.interpolate( 32 | x, scale_factor=float(self.up_scale), mode="bilinear", align_corners=False, recompute_scale_factor=False 33 | ) 34 | 35 | 36 | 37 | 38 | class SpatialSoftArgmax(nn.Module): 39 | """ 40 | The spatial softmax of each feature 41 | map is used to compute a weighted mean of the pixel 42 | locations, effectively performing a soft arg-max 43 | over the feature dimension. 44 | 45 | """ 46 | 47 | def __init__(self, normalize=True): 48 | """Constructor. 49 | Args: 50 | normalize (bool): Whether to use normalized 51 | image coordinates, i.e. coordinates in 52 | the range `[-1, 1]`. 53 | """ 54 | super().__init__() 55 | 56 | self.normalize = normalize 57 | 58 | def _coord_grid(self, h, w, device): 59 | if self.normalize: 60 | return torch.stack( 61 | torch.meshgrid( 62 | torch.linspace(-1, 1, h, device=device), 63 | torch.linspace(-1, 1, w, device=device), 64 | indexing='ij', 65 | ) 66 | ) 67 | return torch.stack( 68 | torch.meshgrid( 69 | torch.arange(0, h, device=device), 70 | torch.arange(0, w, device=device), 71 | indexing='ij', 72 | ) 73 | ) 74 | 75 | def forward(self, x): 76 | assert x.ndim == 4, "Expecting a tensor of shape (B, C, H, W)." 77 | 78 | # compute a spatial softmax over the input: 79 | # given an input of shape (B, C, H, W), 80 | # reshape it to (B*C, H*W) then apply 81 | # the softmax operator over the last dimension 82 | b, c, h, w = x.shape 83 | softmax = F.softmax(x.view(-1, h * w), dim=-1) 84 | 85 | # create a meshgrid of pixel coordinates 86 | # both in the x and y axes 87 | yc, xc = self._coord_grid(h, w, x.device) 88 | 89 | # element-wise multiply the x and y coordinates 90 | # with the softmax, then sum over the h*w dimension 91 | # this effectively computes the weighted mean of x 92 | # and y locations 93 | x_mean = (softmax * xc.flatten()).sum(dim=1, keepdims=True) 94 | y_mean = (softmax * yc.flatten()).sum(dim=1, keepdims=True) 95 | 96 | # concatenate and reshape the result 97 | # to (B, C, 2) where for every feature 98 | # we have the expected x and y pixel 99 | # locations 100 | return torch.cat([x_mean, y_mean], dim=1).view(-1, c, 2) 101 | 102 | 103 | class KeyPointSegNet(nn.Module): 104 | def __init__(self, args, lim=[-1., 1., -1., 1.], use_gpu=True): 105 | super(KeyPointSegNet, self).__init__() 106 | 107 | self.args = args 108 | self.lim = lim 109 | 110 | k = args.n_kp 111 | 112 | if use_gpu: 113 | self.device = "cuda" 114 | else: 115 | self.device = "cpu" 116 | 117 | 118 | deeplabv3_resnet50 = models.segmentation.deeplabv3_resnet50(pretrained=True) 119 | deeplabv3_resnet50.classifier[4] = torch.nn.Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1)) # Change final layer to 2 classes 120 | 121 | self.backbone = torch.nn.Sequential(list(deeplabv3_resnet50.children())[0]) 122 | 123 | self.read_out = KeypointUpSample(2048, k) 124 | 125 | self.spatialsoftargmax = SpatialSoftArgmax() 126 | 127 | self.classifer = torch.nn.Sequential((list(deeplabv3_resnet50.children())[1])) 128 | 129 | 130 | 131 | def forward(self, img): 132 | input_shape = img.shape[-2:] 133 | 134 | resnet_out = self.backbone(img)['out'] # (B, 2048, H//8, W//8) 135 | 136 | # keypoint prediction branch 137 | heatmap = self.read_out(resnet_out) # (B, k, H//4, W//4) 138 | keypoints = self.spatialsoftargmax(heatmap) 139 | # mapping back to original resolution from [-1,1] 140 | offset = torch.tensor([self.lim[0], self.lim[2]], device = resnet_out.device) 141 | scale = torch.tensor([self.args.width // 2, self.args.height // 2], device = resnet_out.device) 142 | keypoints = keypoints - offset 143 | keypoints = keypoints * scale 144 | 145 | # segmentation branch 146 | x = self.classifer(resnet_out) 147 | segout = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=False) 148 | 149 | return keypoints, segout 150 | -------------------------------------------------------------------------------- /models/mesh_renderer.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import numpy as np 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | # io utils 8 | from pytorch3d.io import load_obj 9 | 10 | # datastructures 11 | from pytorch3d.structures import Meshes 12 | 13 | # rendering components 14 | from pytorch3d.renderer import ( 15 | RasterizationSettings, MeshRenderer, MeshRasterizer, BlendParams, 16 | SoftSilhouetteShader, HardPhongShader, PointLights, TexturesVertex, 17 | PerspectiveCameras,Textures 18 | ) 19 | 20 | from os.path import exists 21 | 22 | 23 | class RobotMeshRenderer(): 24 | """ 25 | Class that render robot mesh with differentiable renderer 26 | """ 27 | def __init__(self, focal_length, principal_point, image_size, robot, mesh_files, device): 28 | 29 | self.focal_length = focal_length 30 | self.principal_point = principal_point 31 | self.image_size = image_size 32 | self.device = device 33 | self.robot = robot 34 | self.mesh_files = mesh_files 35 | self.preload_verts = [] 36 | self.preload_faces = [] 37 | 38 | 39 | # preload the mesh to save loading time 40 | for m_file in mesh_files: 41 | assert exists(m_file) 42 | preload_verts_i, preload_faces_idx_i, _ = load_obj(m_file) 43 | preload_faces_i = preload_faces_idx_i.verts_idx 44 | self.preload_verts.append(preload_verts_i) 45 | self.preload_faces.append(preload_faces_i) 46 | 47 | 48 | # set up differentiable renderer with given camera parameters 49 | self.cameras = PerspectiveCameras(focal_length = [focal_length], 50 | principal_point = [principal_point], 51 | device=device, 52 | in_ndc=False, image_size = [image_size]) # (height, width) !!!!! 53 | 54 | blend_params = BlendParams(sigma=1e-8, gamma=1e-8) 55 | raster_settings = RasterizationSettings( 56 | image_size=image_size, 57 | blur_radius=np.log(1. / 1e-4 - 1.) * blend_params.sigma, 58 | faces_per_pixel=100, 59 | max_faces_per_bin=100000, # max_faces_per_bin=1000000, 60 | ) 61 | 62 | # Create a silhouette mesh renderer by composing a rasterizer and a shader. 63 | self.silhouette_renderer = MeshRenderer( 64 | rasterizer=MeshRasterizer( 65 | cameras=self.cameras, 66 | raster_settings=raster_settings 67 | ), 68 | shader=SoftSilhouetteShader(blend_params=blend_params) 69 | ) 70 | 71 | 72 | # We will also create a Phong renderer. This is simpler and only needs to render one face per pixel. 73 | raster_settings = RasterizationSettings( 74 | image_size=image_size, 75 | blur_radius=0.0, 76 | faces_per_pixel=1, 77 | max_faces_per_bin=100000, 78 | ) 79 | # We can add a point light in front of the object. 80 | lights = PointLights(device=device, location=((2.0, 2.0, -2.0),)) 81 | self.phong_renderer = MeshRenderer( 82 | rasterizer=MeshRasterizer( 83 | cameras=self.cameras, 84 | raster_settings=raster_settings 85 | ), 86 | shader=HardPhongShader(device=device, cameras=self.cameras, lights=lights) 87 | ) 88 | 89 | def get_robot_mesh(self, joint_angle): 90 | 91 | R_list, t_list = self.robot.get_joint_RT(joint_angle) 92 | assert len(self.mesh_files) == R_list.shape[0] and len(self.mesh_files) == t_list.shape[0] 93 | 94 | verts_list = [] 95 | faces_list = [] 96 | verts_rgb_list = [] 97 | verts_count = 0 98 | for i in range(len(self.mesh_files)): 99 | verts_i = self.preload_verts[i] 100 | faces_i = self.preload_faces[i] 101 | 102 | R = torch.tensor(R_list[i],dtype=torch.float32) 103 | t = torch.tensor(t_list[i],dtype=torch.float32) 104 | verts_i = verts_i @ R.T + t 105 | #verts_i = (R @ verts_i.T).T + t 106 | faces_i = faces_i + verts_count 107 | 108 | verts_count+=verts_i.shape[0] 109 | 110 | verts_list.append(verts_i.to(self.device)) 111 | faces_list.append(faces_i.to(self.device)) 112 | 113 | # Initialize each vertex to be white in color. 114 | color = torch.rand(3) 115 | verts_rgb_i = torch.ones_like(verts_i) * color # (V, 3) 116 | verts_rgb_list.append(verts_rgb_i.to(self.device)) 117 | 118 | 119 | 120 | verts = torch.concat(verts_list, dim=0) 121 | faces = torch.concat(faces_list, dim=0) 122 | 123 | verts_rgb = torch.concat(verts_rgb_list,dim=0)[None] 124 | textures = Textures(verts_rgb=verts_rgb) 125 | 126 | # Create a Meshes object 127 | robot_mesh = Meshes( 128 | verts=[verts.to(self.device)], 129 | faces=[faces.to(self.device)], 130 | textures=textures 131 | ) 132 | 133 | return robot_mesh 134 | 135 | 136 | def get_robot_verts_and_faces(self, joint_angle): 137 | 138 | R_list, t_list = self.robot.get_joint_RT(joint_angle) 139 | assert len(self.mesh_files) == R_list.shape[0] and len(self.mesh_files) == t_list.shape[0] 140 | 141 | verts_list = [] 142 | faces_list = [] 143 | verts_rgb_list = [] 144 | verts_count = 0 145 | for i in range(len(self.mesh_files)): 146 | verts_i = self.preload_verts[i] 147 | faces_i = self.preload_faces[i] 148 | 149 | R = torch.tensor(R_list[i],dtype=torch.float32) 150 | t = torch.tensor(t_list[i],dtype=torch.float32) 151 | verts_i = verts_i @ R.T + t 152 | #verts_i = (R @ verts_i.T).T + t 153 | faces_i = faces_i + verts_count 154 | 155 | verts_count+=verts_i.shape[0] 156 | 157 | verts_list.append(verts_i.to(self.device)) 158 | faces_list.append(faces_i.to(self.device)) 159 | 160 | # Initialize each vertex to be white in color. 161 | #color = torch.rand(3) 162 | #verts_rgb_i = torch.ones_like(verts_i) * color # (V, 3) 163 | #verts_rgb_list.append(verts_rgb_i.to(self.device)) 164 | 165 | verts = torch.concat(verts_list, dim=0) 166 | faces = torch.concat(faces_list, dim=0) 167 | 168 | 169 | return verts, faces -------------------------------------------------------------------------------- /models/robot_arm.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from roboticstoolbox.robot.ERobot import ERobot 3 | 4 | class BaxterLeftArm(): 5 | def __init__(self, urdf_file): 6 | 7 | self.robot = self.Baxter(urdf_file) 8 | 9 | def get_joint_RT(self, joint_angle): 10 | 11 | assert joint_angle.shape[0] == 7 12 | joint_angle_all = np.zeros(15) 13 | joint_angle_all[-7:] = joint_angle 14 | 15 | link_idx_list = [30,31,32,33,34,36,37] 16 | R_list = [] 17 | t_list = [] 18 | # base:30, J1:30, J2:31, J3:32, J4:33, J5:34, J6:36, J7:37 19 | 20 | for i in range(joint_angle.shape[0]): 21 | link_idx = link_idx_list[i] 22 | T = self.robot.fkine(joint_angle_all, end = self.robot.links[link_idx], start = self.robot.links[30]) 23 | R_list.append(T.R) 24 | t_list.append(T.t) 25 | 26 | 27 | 28 | return np.array(R_list),np.array(t_list) 29 | 30 | 31 | class Baxter(ERobot): 32 | """ 33 | Class that imports a URDF model 34 | """ 35 | 36 | def __init__(self, urdf_file): 37 | 38 | links, name, urdf_string, urdf_filepath = self.URDF_read(urdf_file) 39 | 40 | super().__init__( 41 | links, 42 | name=name, 43 | manufacturer="Rethink", 44 | urdf_string=urdf_string, 45 | urdf_filepath=urdf_filepath, 46 | # gripper_links=elinks[9] 47 | ) 48 | 49 | # self.qdlim = np.array([ 50 | # 2.1750, 2.1750, 2.1750, 2.1750, 2.6100, 2.6100, 2.6100, 3.0, 3.0]) 51 | 52 | 53 | class PandaArm(): 54 | def __init__(self, urdf_file): 55 | 56 | self.robot = self.Panda(urdf_file) 57 | 58 | def get_joint_RT(self, joint_angle): 59 | 60 | assert joint_angle.shape[0] == 7 61 | 62 | 63 | link_idx_list = [0,1,2,3,4,5,6,7,9] 64 | # link 0,1,2,3,4,5,6,7, and hand 65 | R_list = [] 66 | t_list = [] 67 | 68 | 69 | for i in range(len(link_idx_list)): 70 | link_idx = link_idx_list[i] 71 | T = self.robot.fkine(joint_angle, end = self.robot.links[link_idx], start = self.robot.links[0]) 72 | R_list.append(T.R) 73 | t_list.append(T.t) 74 | 75 | 76 | 77 | return np.array(R_list),np.array(t_list) 78 | 79 | 80 | class Panda(ERobot): 81 | """ 82 | Class that imports a URDF model 83 | """ 84 | 85 | def __init__(self, urdf_file): 86 | 87 | links, name, urdf_string, urdf_filepath = self.URDF_read(urdf_file) 88 | 89 | super().__init__( 90 | links, 91 | name=name, 92 | manufacturer="Franka", 93 | urdf_string=urdf_string, 94 | urdf_filepath=urdf_filepath, 95 | # gripper_links=elinks[9] 96 | ) 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /package.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | ctrnet-robot-pose-estimation-ros 4 | 0.0.0 5 | The ctrnet-robot-pose-estimation-ros package 6 | 7 | 8 | 9 | 10 | arclab 11 | 12 | 13 | 14 | 15 | 16 | TODO 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | catkin 52 | roscpp 53 | rospy 54 | std_msgs 55 | roscpp 56 | rospy 57 | std_msgs 58 | roscpp 59 | rospy 60 | std_msgs 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | -------------------------------------------------------------------------------- /ros_nodes/baxter_leftarm_pose.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | base_dir = os.path.abspath(".") 4 | sys.path.append(base_dir) 5 | 6 | import numpy as np 7 | import time 8 | import rospy 9 | from cv_bridge import CvBridge, CvBridgeError 10 | from message_filters import ApproximateTimeSynchronizer, Subscriber 11 | import sensor_msgs 12 | import geometry_msgs 13 | import kornia 14 | 15 | import torch 16 | import torchvision.transforms as transforms 17 | 18 | from PIL import Image as PILImage 19 | from utils import * 20 | from models.CtRNet import CtRNet 21 | 22 | import cv2 23 | bridge = CvBridge() 24 | 25 | #os.environ['ROS_MASTER_URI']='http://192.168.1.116:11311' 26 | #os.environ['ROS_IP']='192.168.1.186' 27 | 28 | ################################################################ 29 | import argparse 30 | parser = argparse.ArgumentParser() 31 | 32 | args = parser.parse_args("") 33 | 34 | args.base_dir = "/home/jingpei/Desktop/CtRNet-robot-pose-estimation" 35 | args.use_gpu = True 36 | args.trained_on_multi_gpus = False 37 | args.keypoint_seg_model_path = os.path.join(args.base_dir,"weights/baxter/net.pth") 38 | args.urdf_file = os.path.join(args.base_dir,"urdfs/Baxter/baxter_description/urdf/baxter.urdf") 39 | args.robot_name = 'Baxter_left_arm' # "Panda" or "Baxter_left_arm" 40 | args.n_kp = 7 41 | args.scale = 0.3125 42 | args.height = 1536 43 | args.width = 2048 44 | args.fx, args.fy, args.px, args.py = 960.41357421875, 960.22314453125, 1021.7171020507812, 776.2381591796875 45 | # scale the camera parameters 46 | args.width = int(args.width * args.scale) 47 | args.height = int(args.height * args.scale) 48 | args.fx = args.fx * args.scale 49 | args.fy = args.fy * args.scale 50 | args.px = args.px * args.scale 51 | args.py = args.py * args.scale 52 | 53 | if args.use_gpu: 54 | device = "cuda" 55 | else: 56 | device = "cpu" 57 | 58 | trans_to_tensor = transforms.Compose([ 59 | transforms.ToTensor(), 60 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 61 | ]) 62 | 63 | CtRNet = CtRNet(args) 64 | 65 | def preprocess_img(cv_img,args): 66 | image_pil = PILImage.fromarray(cv_img) 67 | width, height = image_pil.size 68 | new_size = (int(width*args.scale),int(height*args.scale)) 69 | image_pil = image_pil.resize(new_size) 70 | image = trans_to_tensor(image_pil) 71 | return image 72 | 73 | 74 | #############################################################################3 75 | 76 | #start = time.time() 77 | def gotData(img_msg, joint_msg): 78 | #global start 79 | print("Received data!") 80 | try: 81 | # Convert your ROS Image message to OpenCV2 82 | cv2_img = bridge.imgmsg_to_cv2(img_msg, "bgr8") 83 | cv_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB) 84 | image = preprocess_img(cv_img,args) 85 | 86 | joint_angles = np.array(joint_msg.position)[[4,5,2,3,6,7,8]] 87 | 88 | if args.use_gpu: 89 | image = image.cuda() 90 | 91 | cTr, points_2d, segmentation = CtRNet.inference_single_image(image, joint_angles) 92 | 93 | print(cTr) 94 | qua = kornia.geometry.conversions.angle_axis_to_quaternion(cTr[:,:3]).detach().cpu().numpy().squeeze() # xyzw 95 | #print(qua) 96 | T = cTr[:,3:].detach().cpu().numpy().squeeze() 97 | p = geometry_msgs.msg.PoseStamped() 98 | p.header = img_msg.header 99 | p.pose.position.x = T[0] 100 | p.pose.position.y = T[1] 101 | p.pose.position.z = T[2] 102 | p.pose.orientation.x = qua[0] 103 | p.pose.orientation.y = qua[1] 104 | p.pose.orientation.z = qua[2] 105 | p.pose.orientation.w = qua[3] 106 | #print(p) 107 | pose_pub.publish(p) 108 | 109 | 110 | #### visualization code #### 111 | #points_2d = points_2d.detach().cpu().numpy() 112 | #img_np = to_numpy_img(image) 113 | #img_np = overwrite_image(img_np,points_2d[0].astype(int), color=(1,0,0)) 114 | #plt.imsave("test.png",img_np) 115 | #### 116 | 117 | 118 | except CvBridgeError as e: 119 | print(e) 120 | 121 | 122 | 123 | rospy.init_node('baxter_leftarm_pose') 124 | # Define your image topic 125 | image_topic = "/rgb/image_raw" 126 | robot_joint_topic = "/joint_states" 127 | robot_pose_topic = "robot_pose" 128 | # Set up your subscriber and define its callback 129 | #rospy.Subscriber(image_topic, sensor_msgs.msg.Image, gotData) 130 | 131 | image_sub = Subscriber(image_topic, sensor_msgs.msg.Image) 132 | robot_j_sub = Subscriber(robot_joint_topic, sensor_msgs.msg.JointState) 133 | pose_pub = rospy.Publisher(robot_pose_topic, geometry_msgs.msg.PoseStamped, queue_size=1) 134 | 135 | ats = ApproximateTimeSynchronizer([image_sub, robot_j_sub], queue_size=10, slop=5) 136 | ats.registerCallback(gotData) 137 | 138 | 139 | # Main loop: 140 | rate = rospy.Rate(30) # 30hz 141 | 142 | while not rospy.is_shutdown(): 143 | rate.sleep() -------------------------------------------------------------------------------- /ros_nodes/panda_pose.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import sys 3 | import os 4 | base_dir = os.path.abspath(".") 5 | sys.path.append(base_dir) 6 | 7 | import numpy as np 8 | import time 9 | import rospy 10 | from cv_bridge import CvBridge, CvBridgeError 11 | from message_filters import ApproximateTimeSynchronizer, Subscriber 12 | import sensor_msgs 13 | import geometry_msgs 14 | import kornia 15 | 16 | import torch 17 | import torchvision.transforms as transforms 18 | 19 | from PIL import Image as PILImage 20 | from utils import * 21 | from models.CtRNet import CtRNet 22 | 23 | import cv2 24 | bridge = CvBridge() 25 | 26 | import transforms3d as t3d 27 | import tf2_ros 28 | #os.environ['ROS_MASTER_URI']='http://192.168.1.116:11311' 29 | #os.environ['ROS_IP']='192.168.1.186' 30 | 31 | ################################################################ 32 | import argparse 33 | parser = argparse.ArgumentParser() 34 | 35 | args = parser.parse_args("") 36 | 37 | args.base_dir = "/home/workspace/src/ctrnet-robot-pose-estimation-ros/" 38 | args.use_gpu = True 39 | args.trained_on_multi_gpus = True 40 | args.keypoint_seg_model_path = os.path.join(args.base_dir,"weights/panda/panda-3cam_azure/net.pth") 41 | args.urdf_file = os.path.join(args.base_dir,"urdfs/Panda/panda.urdf") 42 | args.robot_name = 'Panda' # "Panda" or "Baxter_left_arm" 43 | args.n_kp = 7 44 | args.scale = 0.15625 45 | args.height = 1536 46 | args.width = 2048 47 | args.fx, args.fy, args.px, args.py = 960.41357421875, 960.22314453125, 1021.7171020507812, 776.2381591796875 48 | # scale the camera parameters 49 | args.width = int(args.width * args.scale) 50 | args.height = int(args.height * args.scale) 51 | args.fx = args.fx * args.scale 52 | args.fy = args.fy * args.scale 53 | args.px = args.px * args.scale 54 | args.py = args.py * args.scale 55 | 56 | if args.use_gpu: 57 | device = "cuda" 58 | else: 59 | device = "cpu" 60 | 61 | trans_to_tensor = transforms.Compose([ 62 | transforms.ToTensor(), 63 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 64 | ]) 65 | 66 | CtRNet = CtRNet(args) 67 | 68 | def preprocess_img(cv_img,args): 69 | image_pil = PILImage.fromarray(cv_img) 70 | width, height = image_pil.size 71 | new_size = (int(width*args.scale),int(height*args.scale)) 72 | image_pil = image_pil.resize(new_size) 73 | image = trans_to_tensor(image_pil) 74 | return image 75 | 76 | 77 | #############################################################################3 78 | 79 | #start = time.time() 80 | def gotData(img_msg, joint_msg): 81 | #global start 82 | print("Received data!") 83 | try: 84 | # Convert your ROS Image message to OpenCV2 85 | cv2_img = bridge.imgmsg_to_cv2(img_msg, "bgr8") 86 | cv_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB) 87 | image = preprocess_img(cv_img,args) 88 | 89 | joint_angles = np.array(joint_msg.position)[[0,1,2,3,4,5,6]] 90 | 91 | if args.use_gpu: 92 | image = image.cuda() 93 | 94 | cTr, points_2d, segmentation = CtRNet.inference_single_image(image, joint_angles) 95 | #cTb = CtRNet.bpnp(CtRNet.points_2d_pred, CtRNet.points_3d, CtRNet.K).detach().cpu() 96 | print(cTr) 97 | qua = kornia.geometry.conversions.angle_axis_to_quaternion(cTr[:,:3]).detach().cpu().numpy().squeeze() # xyzw 98 | #print(qua) 99 | T = cTr[:,3:].detach().cpu().numpy().squeeze() 100 | p = geometry_msgs.msg.PoseStamped() 101 | p.header = img_msg.header 102 | p.pose.position.x = T[0] 103 | p.pose.position.y = T[1] 104 | p.pose.position.z = T[2] 105 | p.pose.orientation.x = qua[0] 106 | p.pose.orientation.y = qua[1] 107 | p.pose.orientation.z = qua[2] 108 | p.pose.orientation.w = qua[3] 109 | #print(p) 110 | pose_pub.publish(p) 111 | 112 | # Rotating to ROS format 113 | cvTr= np.eye(4) 114 | cvTr[:3, :3] = kornia.geometry.conversions.angle_axis_to_rotation_matrix(cTr[:, :3]).detach().cpu().numpy().squeeze() 115 | cvTr[:3, 3] = np.array(cTr[:, 3:].detach().cpu()) 116 | 117 | # ROS camera to CV camera transform 118 | cTcv = np.array([[0, 0 , 1, 0], [-1, 0, 0 , 0], [0, -1, 0, 0], [0, 0, 0, 1]]) 119 | T = cTcv@cvTr 120 | qua = t3d.quaternions.mat2quat(T[:3, :3]) # wxyz 121 | # Publish Transform 122 | br = tf2_ros.TransformBroadcaster() 123 | t = geometry_msgs.msg.TransformStamped() 124 | t.header.stamp = rospy.Time.now() 125 | t.header.frame_id = "camera_base" 126 | t.child_frame_id = "panda_link0" 127 | t.transform.translation.x = T[0, 3] 128 | t.transform.translation.y = T[1, 3] 129 | t.transform.translation.z = T[2, 3] 130 | t.transform.rotation.x = qua[1] 131 | t.transform.rotation.y = qua[2] 132 | t.transform.rotation.z = qua[3] 133 | t.transform.rotation.w = qua[0] 134 | br.sendTransform(t) 135 | 136 | #### visualization code #### 137 | #points_2d = points_2d.detach().cpu().numpy() 138 | #img_np = to_numpy_img(image) 139 | #img_np = overwrite_image(img_np,points_2d[0].astype(int), color=(1,0,0)) 140 | #plt.imsave("test.png",img_np) 141 | #### 142 | 143 | 144 | except CvBridgeError as e: 145 | print(e) 146 | 147 | 148 | 149 | rospy.init_node('panda_pose') 150 | # Define your image topic 151 | image_topic = "/rgb/image_raw" 152 | robot_joint_topic = "/joint_states" 153 | robot_pose_topic = "robot_pose" 154 | # Set up your subscriber and define its callback 155 | #rospy.Subscriber(image_topic, sensor_msgs.msg.Image, gotData) 156 | 157 | image_sub = Subscriber(image_topic, sensor_msgs.msg.Image) 158 | robot_j_sub = Subscriber(robot_joint_topic, sensor_msgs.msg.JointState) 159 | pose_pub = rospy.Publisher(robot_pose_topic, geometry_msgs.msg.PoseStamped, queue_size=1) 160 | 161 | ats = ApproximateTimeSynchronizer([image_sub, robot_j_sub], queue_size=10, slop=5) 162 | ats.registerCallback(gotData) 163 | 164 | 165 | # Main loop: 166 | rate = rospy.Rate(30) # 30hz 167 | 168 | while not rospy.is_shutdown(): 169 | rate.sleep() -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | ## ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD 2 | from distutils.core import setup 3 | from catkin_pkg.python_setup import generate_distutils_setup 4 | # fetch values from package.xml 5 | setup_args = generate_distutils_setup( 6 | packages=['models', 'utils', 'urdfs', 'weights'], 7 | package_dir={'': '.'}, 8 | ) 9 | setup(**setup_args) -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | base_dir = os.path.abspath(".") 4 | sys.path.append(base_dir) 5 | 6 | import numpy as np 7 | import matplotlib.pyplot as plt 8 | import torch 9 | from torch.utils.data import Dataset, DataLoader 10 | import torchvision.transforms as transforms 11 | import torch.optim as optim 12 | from torch.optim.lr_scheduler import ReduceLROnPlateau 13 | from torch.utils.tensorboard import SummaryWriter 14 | from tqdm import tqdm 15 | from pathlib import Path 16 | 17 | from utils import * 18 | 19 | import argparse 20 | 21 | def get_args(): 22 | parser = argparse.ArgumentParser() 23 | args = parser.parse_args("") 24 | 25 | args.data_folder = '/home/jingpei/Desktop/robot_pose_estimation/data_generation/baxter_data' 26 | args.base_dir = "/home/jingpei/Desktop/CtRNet-robot-pose-estimation" 27 | args.use_gpu = True 28 | args.trained_on_multi_gpus = False 29 | args.keypoint_seg_model_path = os.path.join(args.base_dir,"weights/pretrain/baxter/net.pth") 30 | #args.keypoint_seg_model_path = os.path.join(args.base_dir,"weights/baxter/net.pth") 31 | args.urdf_file = os.path.join(args.base_dir,"urdfs/Baxter/baxter_description/urdf/baxter.urdf") 32 | 33 | ##### training parameters ##### 34 | args.batch_size = 6 35 | args.num_workers = 6 36 | args.lr = 1e-6 37 | args.beta1 = 0.9 38 | args.n_epoch = 500 39 | args.out_dir = 'outputs/Baxter_arm/weights' 40 | args.ckp_per_epoch = 10 41 | args.reproj_err_scale = 1.0 / 100.0 42 | ################################ 43 | 44 | args.robot_name = 'Baxter_left_arm' # "Panda" or "Baxter_left_arm" 45 | args.n_kp = 7 46 | args.scale = 0.3125 47 | args.height = 1536 48 | args.width = 2048 49 | args.fx, args.fy, args.px, args.py = 960.41357421875, 960.22314453125, 1021.7171020507812, 776.2381591796875 50 | 51 | # scale the camera parameters 52 | args.width = int(args.width * args.scale) 53 | args.height = int(args.height * args.scale) 54 | args.fx = args.fx * args.scale 55 | args.fy = args.fy * args.scale 56 | args.px = args.px * args.scale 57 | args.py = args.py * args.scale 58 | 59 | return args 60 | 61 | def main(args): 62 | ######## setup CtRNet ######## 63 | from models.CtRNet import CtRNet 64 | 65 | CtRNet = CtRNet(args) 66 | 67 | mesh_files = [os.path.join(args.base_dir,"urdfs/Baxter/S0/S0.obj"), 68 | os.path.join(args.base_dir,"urdfs/Baxter/S1/S1.obj"), 69 | os.path.join(args.base_dir,"urdfs/Baxter/E0/E0.obj"), 70 | os.path.join(args.base_dir,"urdfs/Baxter/E1/E1.obj"), 71 | os.path.join(args.base_dir,"urdfs/Baxter/W0/W0.obj"), 72 | os.path.join(args.base_dir,"urdfs/Baxter/W1/W1.obj"), 73 | os.path.join(args.base_dir,"urdfs/Baxter/W2/W2.obj")] 74 | 75 | robot_renderer = CtRNet.setup_robot_renderer(mesh_files) 76 | 77 | 78 | ######## setup dataset ######## 79 | from imageloaders.baxter import ImageDataLoaderReal 80 | 81 | trans_to_tensor = transforms.Compose([ 82 | transforms.ToTensor(), 83 | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), 84 | ]) 85 | 86 | datasets = {} 87 | dataloaders = {} 88 | data_n_batches = {} 89 | for phase in ['train','eval']: 90 | datasets[phase] = ImageDataLoaderReal(data_folder = args.data_folder, scale = args.scale, trans_to_tensor = trans_to_tensor) 91 | 92 | 93 | dataloaders[phase] = DataLoader( 94 | datasets[phase], batch_size=args.batch_size, 95 | shuffle=True if phase == 'train' else False, 96 | num_workers=args.num_workers) 97 | 98 | data_n_batches[phase] = len(dataloaders[phase]) 99 | 100 | ######## setup optimizer and criterions ######## 101 | 102 | criterionMSE_sum = torch.nn.MSELoss(reduction='sum') 103 | criterionMSE_mean = torch.nn.MSELoss(reduction='mean') 104 | criterionBCE = torch.nn.BCEWithLogitsLoss() 105 | criterions = {"mse_sum": criterionMSE_sum, "mse_mean": criterionMSE_mean, "bce": criterionBCE} 106 | 107 | optimizer = optim.Adam(CtRNet.keypoint_seg_predictor.parameters(), lr=args.lr, betas=(args.beta1, 0.999)) 108 | scheduler = ReduceLROnPlateau(optimizer, 'min', factor=0.1, patience=5, verbose=True) 109 | 110 | ######## training loop ######## 111 | epoch_writer = SummaryWriter(comment="_writter") 112 | 113 | best_valid_loss = np.inf 114 | 115 | for epoch in range(0, args.n_epoch): 116 | phases = ['train','eval'] 117 | 118 | for phase in phases: 119 | iter_writer = SummaryWriter(comment="_epoch_" + str(epoch) + "_" + phase) 120 | 121 | # set model to train/eval mode 122 | CtRNet.keypoint_seg_predictor.train(phase == 'train') 123 | print("model training: " + str(CtRNet.keypoint_seg_predictor.training)) 124 | 125 | 126 | meter_loss = AverageMeter() 127 | 128 | loader = dataloaders[phase] 129 | 130 | for i, data in tqdm(enumerate(loader), total=data_n_batches[phase]): 131 | 132 | if args.use_gpu: 133 | if isinstance(data, list): 134 | data = [d.cuda() for d in data] 135 | else: 136 | data = data.cuda() 137 | 138 | # load data 139 | img, joint_angles = data 140 | 141 | # forward 142 | loss = CtRNet.train_on_batch(img, joint_angles.cpu().squeeze(), robot_renderer, criterions, phase) 143 | meter_loss.update(loss.item(), n=img.size(0)) 144 | 145 | if phase == 'train': 146 | optimizer.zero_grad() 147 | loss.backward() 148 | torch.nn.utils.clip_grad_value_(CtRNet.keypoint_seg_predictor.parameters(), 10) 149 | optimizer.step() 150 | 151 | # write to log 152 | iter_writer.add_scalar('loss_all', loss.item(), i) 153 | 154 | log = '%s [%d/%d] Loss: %.6f, LR: %f' % ( 155 | phase, epoch, args.n_epoch, 156 | meter_loss.avg, 157 | get_lr(optimizer)) 158 | 159 | iter_writer.close() 160 | 161 | print(log) 162 | if phase == 'train': 163 | epoch_writer.add_scalar('loss_train', meter_loss.avg, epoch) 164 | else: 165 | epoch_writer.add_scalar('loss_eval', meter_loss.avg, epoch) 166 | 167 | 168 | if phase == 'eval': 169 | scheduler.step(meter_loss.avg) 170 | if meter_loss.avg < best_valid_loss: 171 | best_valid_loss = meter_loss.avg 172 | 173 | torch.save(CtRNet.keypoint_seg_predictor.state_dict(), '%s/net_best.pth' % (args.out_dir)) 174 | 175 | log = 'Best eval: %.6f' % (best_valid_loss) 176 | print(log) 177 | torch.save(CtRNet.keypoint_seg_predictor.state_dict(), '%s/net_last.pth' % (args.out_dir)) 178 | 179 | epoch_writer.close() 180 | 181 | 182 | if __name__ == '__main__': 183 | args = get_args() 184 | if args.out_dir: 185 | Path(args.out_dir).mkdir(parents=True, exist_ok=True) 186 | main(args) -------------------------------------------------------------------------------- /urdfs/Baxter/E0/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:18:11 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.512 0.01365332 0.01365332 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/E1/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:19:01 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.512 0 0 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/S0/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/08/2022 20:09:18 4 | 5 | newmtl Material_002 6 | Ka 0.1 0.1 0.1 7 | Kd 0.09748456 0.09748456 0.09748456 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/S1/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:16:20 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.64 0 0 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.5 0.5 0.5 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/W0/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:17:56 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.512 0 0 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/W1/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:18:31 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.09748472 0.09748472 0.09748472 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/W2/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 06/09/2022 21:17:39 4 | 5 | newmtl Material_001 6 | Ka 0.1 0.1 0.1 7 | Kd 0.01630304 0.01630304 0.01630304 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 50 13 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 2.8.3) 2 | project(baxter_description) 3 | 4 | find_package(catkin REQUIRED) 5 | 6 | catkin_package() 7 | 8 | foreach(dir meshes urdf) 9 | install(DIRECTORY ${dir}/ 10 | DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}/${dir}) 11 | endforeach(dir) 12 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/meshes/base/PEDESTAL.STL: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/ucsdarclab/CtRNet-robot-pose-estimation/05156c5ed5ac4bd67e96b51dc72238ff2743c388/urdfs/Baxter/baxter_description/meshes/wrist/W2.STL -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/package.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | baxter_description 4 | 1.2.0 5 | 6 | Description of Baxter Research Robot from Rethink Robotics. 7 | This package contains the URDF and meshes describing Baxter. 8 | 9 | 10 | 11 | Rethink Robotics Inc. 12 | 13 | BSD 14 | http://sdk.rethinkrobotics.com 15 | 16 | https://github.com/RethinkRobotics/baxter_common 17 | 18 | 19 | https://github.com/RethinkRobotics/baxter_common/issues 20 | 21 | Rethink Robotics Inc. 22 | 23 | catkin 24 | rethink_ee_description 25 | 26 | 27 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/urdf/baxter.urdf.xacro: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/urdf/baxter_base/baxter_base.gazebo.xacro: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | /robot 7 | 8 | 9 | 10 | 11 | true 12 | 13 | 14 | 1 15 | 16 | 17 | 1 18 | 19 | 20 | 1 21 | 22 | 23 | 1 24 | 25 | 26 | 1 27 | 28 | 29 | 1 30 | 31 | 32 | 1 33 | 34 | 35 | 1 36 | 37 | 38 | 1 39 | 40 | 41 | 1 42 | 43 | 44 | 1 45 | 46 | 47 | 1 48 | 49 | 50 | 1 51 | 52 | 53 | 1 54 | 55 | 56 | 1 57 | 58 | 59 | 60 | 61 | 30.0 62 | 63 | 0.0 0.21 0.0 0.0 -0.8 -1.570796327 64 | 1.3962634 65 | 66 | 800 67 | 800 68 | R8G8B8 69 | 70 | 71 | 0.02 72 | 300 73 | 74 | 75 | gaussian 76 | 0.0 77 | 0.007 78 | 79 | 80 | 81 | true 82 | 0.0 83 | head_camera 84 | image 85 | camera_info 86 | head_camera 87 | 0.07 88 | 0.0 89 | 0.0 90 | 0.0 91 | 0.0 92 | 0.0 93 | /cameras 94 | 95 | 96 | 97 | 98 | 99 | 30.0 100 | 101 | 0.0 0.0 0.0 0.0 -1.570796327 1.570796327 102 | 1.3962634 103 | 104 | 800 105 | 800 106 | R8G8B8 107 | 108 | 109 | 0.02 110 | 300 111 | 112 | 113 | gaussian 114 | 0.0 115 | 0.007 116 | 117 | 118 | 119 | true 120 | 0.0 121 | right_hand_camera 122 | image 123 | camera_info 124 | right_hand_camera 125 | 0.07 126 | 0.0 127 | 0.0 128 | 0.0 129 | 0.0 130 | 0.0 131 | /cameras 132 | 133 | 134 | 135 | 136 | 137 | 30.0 138 | 139 | 0.0 0.0 0.0 0.0 -1.570796327 1.570796327 140 | 1.3962634 141 | 142 | 800 143 | 800 144 | R8G8B8 145 | 146 | 147 | 0.02 148 | 300 149 | 150 | 151 | gaussian 152 | 0.0 153 | 0.007 154 | 155 | 156 | 157 | true 158 | 0.0 159 | left_hand_camera 160 | image 161 | camera_info 162 | left_hand_camera 163 | 0.07 164 | 0.0 165 | 0.0 166 | 0.0 167 | 0.0 168 | 0.0 169 | /cameras 170 | 171 | 172 | 173 | 174 | 175 | 176 | 600 177 | 1024 178 | /robot/xdisplay 179 | 180 | 181 | 182 | 183 | 184 | 0.0 0.0 0.0 0.0 0.0 0.0 185 | 186 | 187 | 188 | 12 189 | 1.0 190 | -3.14 191 | 3.14 192 | 193 | 194 | 2 195 | 1.0 196 | -0.001 197 | 0 198 | 199 | 200 | 201 | 0.05 202 | 50.0 203 | 204 | 205 | 206 | 0.00 207 | true 208 | 100.0 209 | /robot/sonar/head_sonar/state 210 | sonar_ring 211 | 212 | true 213 | 100.0 214 | 215 | 216 | 217 | 218 | 0.0 0.0 0.0 0.0 0.0 0.0 219 | 220 | 221 | 222 | 1 223 | 1.0 224 | -0.5 225 | 0.5 226 | 227 | 228 | 229 | 0.004 230 | 0.4 231 | 232 | 233 | 234 | 0.005 235 | true 236 | 100 237 | /sim/laserscan/right_hand_range/state 238 | right_hand_range 239 | 240 | true 241 | 100.0 242 | 243 | 244 | 245 | 246 | 0.0 0.0 0.0 0.0 0.0 0.0 247 | 248 | 249 | 250 | 1 251 | 1.0 252 | -0.5 253 | 0.5 254 | 255 | 256 | 257 | 0.004 258 | 0.4 259 | 260 | 261 | 262 | 0.005 263 | true 264 | 100 265 | /sim/laserscan/left_hand_range/state 266 | left_hand_range 267 | 268 | true 269 | 100.0 270 | 271 | 272 | 273 | 274 | 275 | 276 | transmission_interface/SimpleTransmission 277 | 278 | EffortJointInterface 279 | 280 | 281 | EffortJointInterface 282 | 1 283 | 284 | 285 | 286 | transmission_interface/SimpleTransmission 287 | 288 | EffortJointInterface 289 | 290 | 291 | EffortJointInterface 292 | 1 293 | 294 | 295 | 296 | transmission_interface/SimpleTransmission 297 | 298 | EffortJointInterface 299 | 300 | 301 | EffortJointInterface 302 | 1 303 | 304 | 305 | 306 | transmission_interface/SimpleTransmission 307 | 308 | EffortJointInterface 309 | 310 | 311 | EffortJointInterface 312 | 1 313 | 314 | 315 | 316 | transmission_interface/SimpleTransmission 317 | 318 | EffortJointInterface 319 | 320 | 321 | EffortJointInterface 322 | 1 323 | 324 | 325 | 326 | transmission_interface/SimpleTransmission 327 | 328 | EffortJointInterface 329 | 330 | 331 | EffortJointInterface 332 | 1 333 | 334 | 335 | 336 | transmission_interface/SimpleTransmission 337 | 338 | EffortJointInterface 339 | 340 | 341 | EffortJointInterface 342 | 1 343 | 344 | 345 | 346 | 347 | 348 | transmission_interface/SimpleTransmission 349 | 350 | EffortJointInterface 351 | 352 | 353 | EffortJointInterface 354 | 1 355 | 356 | 357 | 358 | transmission_interface/SimpleTransmission 359 | 360 | EffortJointInterface 361 | 362 | 363 | EffortJointInterface 364 | 1 365 | 366 | 367 | 368 | transmission_interface/SimpleTransmission 369 | 370 | EffortJointInterface 371 | 372 | 373 | EffortJointInterface 374 | 1 375 | 376 | 377 | 378 | transmission_interface/SimpleTransmission 379 | 380 | EffortJointInterface 381 | 382 | 383 | EffortJointInterface 384 | 1 385 | 386 | 387 | 388 | transmission_interface/SimpleTransmission 389 | 390 | EffortJointInterface 391 | 392 | 393 | EffortJointInterface 394 | 1 395 | 396 | 397 | 398 | transmission_interface/SimpleTransmission 399 | 400 | EffortJointInterface 401 | 402 | 403 | EffortJointInterface 404 | 1 405 | 406 | 407 | 408 | transmission_interface/SimpleTransmission 409 | 410 | EffortJointInterface 411 | 412 | 413 | EffortJointInterface 414 | 1 415 | 416 | 417 | 418 | 419 | transmission_interface/SimpleTransmission 420 | 421 | EffortJointInterface 422 | 423 | 424 | EffortJointInterface 425 | 1 426 | 427 | 428 | 429 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/urdf/left_end_effector.urdf.xacro: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/urdf/pedestal/pedestal.xacro: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | -------------------------------------------------------------------------------- /urdfs/Baxter/baxter_description/urdf/right_end_effector.urdf.xacro: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 13 | 14 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/collision/finger.obj: -------------------------------------------------------------------------------- 1 | # File produced by Open Asset Import Library (http://www.assimp.sf.net) 2 | # (assimp v3.0.1262) 3 | 4 | mtllib finger.stl.obj.mtl 5 | 6 | # 96 vertex positions 7 | v 0.01036 0.0264034 0.000154629 8 | v 0.0104486 0.0025833 0.000146801 9 | v -0.0103872 0.00253418 0.000131696 10 | v -0.0103872 0.00253418 0.000131696 11 | v -0.0104795 0.0161016 0.0189882 12 | v -0.0104013 0.0263094 0.00016651 13 | v -0.0104013 0.0263094 0.00016651 14 | v -0.0104795 0.0161016 0.0189882 15 | v -0.0103889 0.0252203 0.0191876 16 | v 0.0104486 0.0025833 0.000146801 17 | v -0.0087304 -2.35252e-05 0.0361648 18 | v -0.0103872 0.00253418 0.000131696 19 | v 0.0104005 0.0252534 0.0190366 20 | v -0.0103889 0.0252203 0.0191876 21 | v 0.00583983 0.0142743 0.0538035 22 | v -0.0103872 0.00253418 0.000131696 23 | v -0.0104013 0.0263094 0.00016651 24 | v 0.01036 0.0264034 0.000154629 25 | v 0.00861608 0.0139887 0.0513279 26 | v 0.0104948 0.0151026 0.0184356 27 | v 0.0104005 0.0252534 0.0190366 28 | v 0.0104948 0.0151026 0.0184356 29 | v 0.00869277 -0.000132643 0.0501662 30 | v 0.0104486 0.0025833 0.000146801 31 | v 0.00861608 0.0139887 0.0513279 32 | v 0.00869277 -0.000132643 0.0501662 33 | v 0.0104948 0.0151026 0.0184356 34 | v -0.00862294 -5.68019e-05 0.0509528 35 | v -0.0103872 0.00253418 0.000131696 36 | v -0.0087304 -2.35252e-05 0.0361648 37 | v 0.0104486 0.0025833 0.000146801 38 | v 0.00869277 -0.000132643 0.0501662 39 | v -0.0087304 -2.35252e-05 0.0361648 40 | v 0.00869277 -0.000132643 0.0501662 41 | v -0.00548142 -9.11208e-05 0.0537247 42 | v -0.0087304 -2.35252e-05 0.0361648 43 | v 0.0104005 0.0252534 0.0190366 44 | v 0.0104948 0.0151026 0.0184356 45 | v 0.0104486 0.0025833 0.000146801 46 | v 0.0104005 0.0252534 0.0190366 47 | v 0.0104486 0.0025833 0.000146801 48 | v 0.01036 0.0264034 0.000154629 49 | v 0.0104005 0.0252534 0.0190366 50 | v 0.01036 0.0264034 0.000154629 51 | v -0.0103889 0.0252203 0.0191876 52 | v -0.0103889 0.0252203 0.0191876 53 | v -0.00527792 0.0142931 0.053849 54 | v 0.00583983 0.0142743 0.0538035 55 | v -0.00777838 0.0142182 0.0523659 56 | v -0.00527792 0.0142931 0.053849 57 | v -0.0103889 0.0252203 0.0191876 58 | v -0.00862294 -5.68019e-05 0.0509528 59 | v -0.00548142 -9.11208e-05 0.0537247 60 | v -0.00777838 0.0142182 0.0523659 61 | v -0.0103872 0.00253418 0.000131696 62 | v -0.00862294 -5.68019e-05 0.0509528 63 | v -0.0104795 0.0161016 0.0189882 64 | v 0.0104005 0.0252534 0.0190366 65 | v 0.00583983 0.0142743 0.0538035 66 | v 0.00861608 0.0139887 0.0513279 67 | v 0.01036 0.0264034 0.000154629 68 | v -0.0104013 0.0263094 0.00016651 69 | v -0.0103889 0.0252203 0.0191876 70 | v -0.0104795 0.0161016 0.0189882 71 | v -0.00884117 0.0139176 0.0505894 72 | v -0.0103889 0.0252203 0.0191876 73 | v -0.00884117 0.0139176 0.0505894 74 | v -0.00777838 0.0142182 0.0523659 75 | v -0.0103889 0.0252203 0.0191876 76 | v 0.00583983 0.0142743 0.0538035 77 | v -0.00548142 -9.11208e-05 0.0537247 78 | v 0.00613802 -2.06026e-05 0.0535776 79 | v -0.00527792 0.0142931 0.053849 80 | v -0.00548142 -9.11208e-05 0.0537247 81 | v 0.00583983 0.0142743 0.0538035 82 | v 0.00869277 -0.000132643 0.0501662 83 | v 0.00613802 -2.06026e-05 0.0535776 84 | v -0.00548142 -9.11208e-05 0.0537247 85 | v 0.00613802 -2.06026e-05 0.0535776 86 | v 0.00869277 -0.000132643 0.0501662 87 | v 0.00861608 0.0139887 0.0513279 88 | v -0.00548142 -9.11208e-05 0.0537247 89 | v -0.00862294 -5.68019e-05 0.0509528 90 | v -0.0087304 -2.35252e-05 0.0361648 91 | v -0.00777838 0.0142182 0.0523659 92 | v -0.00548142 -9.11208e-05 0.0537247 93 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vn 0.187376 -0.949866 0.250288 261 | vn -0.0299751 -0.962324 0.270247 262 | vn -0.733474 -0.46004 0.500378 263 | vn -0.819413 -0.278244 0.501142 264 | vn -0.818885 -0.280329 0.500842 265 | vn -0.840131 -0.200724 0.503876 266 | vn -0.865477 -0.0010483 0.500948 267 | vn -0.861767 -0.077237 0.501391 268 | vn -0.734892 0.455474 0.502471 269 | vn -0.789187 0.35685 0.499842 270 | vn -0.899886 0.416094 0.130652 271 | vn -0.934904 0.343772 0.0881805 272 | vn -0.750321 -0.110129 0.651836 273 | vn -0.744296 -0.138475 0.653336 274 | vn -0.0486186 -0.96337 0.263731 275 | vn -0.0569915 -0.952558 0.298973 276 | vn -0.0786935 -0.945844 0.314939 277 | vn 0.0865387 -0.962264 0.257989 278 | vn 0.0988769 -0.988653 0.113084 279 | vn -0.040165 -0.954927 0.294112 280 | vn -0.679455 -0.364641 0.636692 281 | vn -0.676925 -0.535527 0.504959 282 | vn -0.860556 -0.0952615 0.500368 283 | vn -0.866101 -0.0391639 0.498333 284 | vn -0.848661 -0.170347 0.500757 285 | vn -0.976331 0.171097 0.132301 286 | vn -0.784297 0.365988 0.500931 287 | vn -0.743675 0.147478 0.652072 288 | vn -0.75636 0.0667582 0.650741 289 | vn -0.726694 0.470061 0.500957 290 | vn -0.946042 0.295169 0.133715 291 | vn -0.699993 0.303725 0.646344 292 | vn -0.690574 0.326044 0.645603 293 | vn -0.722223 0.238827 0.64912 294 | vn -0.862635 0.0731211 0.500513 295 | vn -0.860436 0.0839585 0.502594 296 | vn -0.823144 0.266065 0.501641 297 | vn -0.850508 0.155797 0.502359 298 | vn -0.850275 0.158766 0.501823 299 | vn -0.824425 0.262885 0.501214 300 | vn -0.000104827 0.000138107 -1 301 | vn -6.4345e-05 4.8788e-05 -1 302 | vn -0.000255153 -5.05889e-05 -1 303 | vn 8.56579e-06 1.35717e-06 -1 304 | vn 3.45225e-06 -5.63502e-06 -1 305 | vn 0.000304041 -4.54974e-06 -1 306 | vn 0.0004587 -0.000166978 -1 307 | vn -4.30629e-05 3.84989e-05 -1 308 | vn -0.000206776 -3.99625e-05 -1 309 | vn -7.87823e-05 -3.27836e-06 -1 310 | vn -0.000409956 0.000110369 -1 311 | vn 6.31049e-05 7.3988e-06 -1 312 | vn -0.0003035 2.77655e-05 -1 313 | 314 | # Mesh '' with 200 faces 315 | g 316 | usemtl DefaultMaterial 317 | f 1//1 2//1 3//1 318 | f 4//2 2//2 1//2 319 | f 3//3 5//3 1//3 320 | f 6//4 7//4 8//4 321 | f 9//5 1//5 10//5 322 | f 1//6 11//6 10//6 323 | f 12//7 13//7 14//7 324 | f 13//7 15//8 14//7 325 | f 2//9 13//9 3//9 326 | f 16//10 9//10 17//10 327 | f 14//11 18//11 19//11 328 | f 20//12 2//12 4//12 329 | f 21//13 20//13 4//13 330 | f 22//14 23//14 4//14 331 | f 23//15 21//15 4//15 332 | f 1//16 5//16 11//16 333 | f 13//17 24//17 15//17 334 | f 20//18 21//18 24//18 335 | f 25//19 26//19 14//19 336 | f 15//20 27//20 14//20 337 | f 28//21 29//21 14//20 338 | f 14//22 30//22 25//22 339 | f 14//22 31//23 12//23 340 | f 32//24 25//24 30//24 341 | f 33//25 17//25 14//25 342 | f 34//26 35//26 14//26 343 | f 36//27 37//27 14//27 344 | f 14//28 37//28 34//28 345 | f 38//29 33//29 14//29 346 | f 19//30 38//30 14//30 347 | f 39//31 18//31 40//31 348 | f 7//32 6//32 41//32 349 | f 42//33 43//33 44//33 350 | f 45//34 43//34 42//34 351 | f 4//35 46//35 47//35 352 | f 17//36 31//36 14//36 353 | f 9//37 31//37 17//37 354 | f 10//38 31//38 9//38 355 | f 11//39 31//39 10//39 356 | f 12//40 31//40 11//40 357 | f 21//41 23//41 48//41 358 | f 13//42 49//42 3//42 359 | f 12//43 49//43 13//43 360 | f 25//44 32//44 50//44 361 | f 14//45 29//45 51//45 362 | f 52//46 30//46 14//46 363 | f 27//47 52//47 14//47 364 | f 53//48 32//48 30//48 365 | f 9//49 54//49 1//49 366 | f 35//50 55//50 14//50 367 | f 56//51 44//51 43//51 368 | f 57//52 47//52 46//52 369 | f 4//53 47//53 57//53 370 | f 58//54 46//54 4//54 371 | f 53//55 46//55 58//55 372 | f 57//56 22//56 4//56 373 | f 57//57 59//57 22//57 374 | f 59//58 15//58 22//58 375 | f 60//59 61//59 62//59 376 | f 24//60 48//60 15//60 377 | f 24//61 21//61 48//61 378 | f 49//62 5//62 3//62 379 | f 5//63 12//63 11//63 380 | f 50//64 4//64 45//64 381 | f 4//65 50//65 58//65 382 | f 32//66 53//66 58//66 383 | f 42//67 26//67 25//67 384 | f 52//68 27//68 63//68 385 | f 9//69 16//69 54//69 386 | f 37//70 64//70 34//70 387 | f 38//71 19//71 65//71 388 | f 40//72 6//72 39//72 389 | f 8//73 66//73 6//73 390 | f 6//74 66//74 39//74 391 | f 18//75 67//75 19//75 392 | f 68//76 19//76 67//76 393 | f 67//77 69//77 68//77 394 | f 40//78 70//78 55//78 395 | f 71//79 72//79 60//79 396 | f 73//80 74//80 35//80 397 | f 55//81 75//81 40//81 398 | f 57//82 46//82 59//82 399 | f 76//83 77//83 78//83 400 | f 62//84 77//84 76//84 401 | f 79//85 44//85 56//85 402 | f 80//86 24//86 13//86 403 | f 24//87 80//87 20//87 404 | f 2//88 80//88 13//88 405 | f 20//89 80//89 2//89 406 | f 15//90 48//90 22//90 407 | f 22//91 48//91 23//91 408 | f 49//92 12//92 5//92 409 | f 25//93 50//93 42//93 410 | f 58//94 50//94 32//94 411 | f 42//95 81//95 26//95 412 | f 59//96 46//96 63//96 413 | f 59//97 63//97 27//97 414 | f 82//98 54//98 16//98 415 | f 82//99 83//99 54//99 416 | f 54//100 83//100 1//100 417 | f 16//101 84//101 82//101 418 | f 33//102 84//102 16//102 419 | f 39//103 67//103 18//103 420 | f 69//104 67//104 39//104 421 | f 8//105 39//105 66//105 422 | f 1//106 39//106 8//106 423 | f 69//107 39//107 1//107 424 | f 40//108 18//108 70//108 425 | f 70//109 18//109 14//109 426 | f 55//110 70//110 14//110 427 | f 72//111 75//111 85//111 428 | f 7//112 41//112 71//112 429 | f 60//113 72//113 61//113 430 | f 59//114 27//114 15//114 431 | f 62//115 61//115 86//115 432 | f 86//116 77//116 62//116 433 | f 87//117 88//117 89//117 434 | f 51//118 90//118 14//118 435 | f 42//119 50//119 45//119 436 | f 14//120 26//120 28//120 437 | f 42//121 44//121 81//121 438 | f 44//122 91//122 81//122 439 | f 53//123 63//123 46//123 440 | f 52//124 53//124 30//124 441 | f 63//125 53//125 52//125 442 | f 33//126 16//126 17//126 443 | f 65//127 84//127 38//127 444 | f 33//128 38//128 84//128 445 | f 69//129 1//129 92//129 446 | f 83//130 92//130 1//130 447 | f 75//131 72//131 93//131 448 | f 93//132 72//132 71//132 449 | f 6//133 40//133 75//133 450 | f 41//134 93//134 71//134 451 | f 85//135 55//135 35//135 452 | f 74//136 85//136 35//136 453 | f 87//137 94//137 95//137 454 | f 36//138 96//138 78//138 455 | f 76//139 96//139 88//139 456 | f 78//140 96//140 76//140 457 | f 88//141 96//141 89//141 458 | f 97//142 79//142 56//142 459 | f 29//143 91//143 98//143 460 | f 28//144 91//144 29//144 461 | f 26//145 81//145 28//145 462 | f 91//146 28//146 81//146 463 | f 73//147 64//147 86//147 464 | f 65//148 82//148 84//148 465 | f 92//149 65//149 99//149 466 | f 93//150 41//150 75//150 467 | f 72//151 85//151 61//151 468 | f 61//152 85//152 74//152 469 | f 61//153 74//153 73//153 470 | f 36//154 78//154 37//154 471 | f 37//155 77//155 64//155 472 | f 98//156 91//156 79//156 473 | f 79//157 100//157 98//157 474 | f 97//158 101//158 79//158 475 | f 95//159 101//159 97//159 476 | f 64//160 73//160 34//160 477 | f 73//161 35//161 34//161 478 | f 69//162 92//162 99//162 479 | f 19//163 69//163 99//163 480 | f 68//164 69//164 19//164 481 | f 92//165 82//165 65//165 482 | f 92//166 83//166 82//166 483 | f 99//167 65//167 19//167 484 | f 85//168 75//168 55//168 485 | f 41//169 6//169 75//169 486 | f 64//170 77//170 86//170 487 | f 77//171 37//171 78//171 488 | f 86//172 61//172 73//172 489 | f 87//173 89//173 94//173 490 | f 79//174 101//174 100//174 491 | f 102//175 90//175 51//175 492 | f 36//176 14//176 90//176 493 | f 91//177 44//177 79//177 494 | f 95//178 94//178 101//178 495 | f 29//179 98//179 51//179 496 | f 100//180 51//180 98//180 497 | f 102//181 51//181 100//181 498 | f 89//182 96//182 36//182 499 | f 36//183 90//183 89//183 500 | f 101//184 102//184 100//184 501 | f 94//185 89//185 102//185 502 | f 90//186 102//186 89//186 503 | f 101//187 94//187 102//187 504 | f 4//188 1//188 87//188 505 | f 4//189 87//189 45//189 506 | f 1//190 8//190 87//190 507 | f 60//191 87//191 71//191 508 | f 56//192 87//191 97//192 509 | f 76//193 87//193 62//193 510 | f 56//194 43//194 87//194 511 | f 45//195 87//195 43//195 512 | f 8//196 7//196 87//196 513 | f 62//197 87//197 60//197 514 | f 97//198 87//198 95//198 515 | f 71//199 87//199 7//199 516 | f 88//200 87//200 76//200 517 | 518 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/finger/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:32:00 4 | 5 | newmtl Part__Feature_007 6 | Ka 0 0 0 7 | Kd 0.2509804 0.2509804 0.2509804 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.5 0.5 0.5 12 | Ns 0 13 | newmtl Part__Feature001_006 14 | Ka 0 0 0 15 | Kd 0.9019608 0.9215686 0.9294118 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.5 0.5 0.5 20 | Ns 0 21 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/hand/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:31:49 4 | 5 | newmtl Part__Feature002_005_005 6 | Ka 0 0 0 7 | Kd 0.9019608 0.9215686 0.9294118 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.03125 0.03125 0.03125 12 | Ns 0 13 | newmtl Part__Feature005_001_005 14 | Ka 0 0 0 15 | Kd 1 1 1 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.03125 0.03125 0.03125 20 | Ns 0 21 | newmtl Part__Feature_009_005 22 | Ka 0 0 0 23 | Kd 0.2509804 0.2509804 0.2509804 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.03125 0.03125 0.03125 28 | Ns 0 29 | newmtl Part__Feature001_008_005 30 | Ka 0 0 0 31 | Kd 0.2509804 0.2509804 0.2509804 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.015625 0.015625 0.015625 36 | Ns 0 37 | newmtl Part__Feature005_001_005_001 38 | Ka 0 0 0 39 | Kd 0.9019608 0.9215686 0.9294118 40 | d 1 41 | Tr 0 42 | illum 2 43 | Ks 0.03125 0.03125 0.03125 44 | Ns 0 45 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link0/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:31:31 4 | 5 | newmtl Part__Feature017_001 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 1 1 1 12 | Ns 0 13 | newmtl Part__Feature022_001 14 | Ka 0 0 0 15 | Kd 0.9019608 0.9215686 0.9294118 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.25 0.25 0.25 20 | Ns 0 21 | newmtl Part__Feature023_001 22 | Ka 0 0 0 23 | Kd 0.2509804 0.2509804 0.2509804 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.25 0.25 0.25 28 | Ns 0 29 | newmtl Part__Feature019_001 30 | Ka 0 0 0 31 | Kd 1 1 1 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.25 0.25 0.25 36 | Ns 0 37 | newmtl Part__Feature018_001 38 | Ka 0 0 0 39 | Kd 1 1 1 40 | d 1 41 | Tr 0 42 | illum 2 43 | Ks 1 1 1 44 | Ns 0 45 | newmtl Face636_001 46 | Ka 0 0 0 47 | Kd 0.9019608 0.9215686 0.9294118 48 | d 1 49 | Tr 0 50 | illum 2 51 | Ks 0.25 0.25 0.25 52 | Ns 0 53 | newmtl Shell001_001 54 | Ka 0 0 0 55 | Kd 0.2509804 0.2509804 0.2509804 56 | d 1 57 | Tr 0 58 | illum 2 59 | Ks 0.25 0.25 0.25 60 | Ns 0 61 | newmtl Shell002_001 62 | Ka 0 0 0 63 | Kd 0.9019608 0.9215686 0.9294118 64 | d 1 65 | Tr 0 66 | illum 2 67 | Ks 0.25 0.25 0.25 68 | Ns 0 69 | newmtl Shell_001 70 | Ka 0 0 0 71 | Kd 0.2509804 0.2509804 0.2509804 72 | d 1 73 | Tr 0 74 | illum 2 75 | Ks 0.25 0.25 0.25 76 | Ns 0 77 | newmtl Shell003_001 78 | Ka 0 0 0 79 | Kd 0.9019608 0.9215686 0.9294118 80 | d 1 81 | Tr 0 82 | illum 2 83 | Ks 0.25 0.25 0.25 84 | Ns 0 85 | newmtl Shell009_001 86 | Ka 0 0 0 87 | Kd 0.2509804 0.2509804 0.2509804 88 | d 1 89 | Tr 0 90 | illum 2 91 | Ks 0.25 0.25 0.25 92 | Ns 0 93 | newmtl Shell010_001 94 | Ka 0 0 0 95 | Kd 0.9019608 0.9215686 0.9294118 96 | d 1 97 | Tr 0 98 | illum 2 99 | Ks 0.25 0.25 0.25 100 | Ns 0 101 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link1/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:31:16 4 | 5 | newmtl Part__Feature_001 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.125 0.125 0.125 12 | Ns 0 13 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link2/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:31:03 4 | 5 | newmtl Part__Feature024 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.25 0.25 0.25 12 | Ns 0 13 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link3/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:30:47 4 | 5 | newmtl Part__Feature_001_001_001_002 6 | Ka 0 0 0 7 | Kd 0.2509804 0.2509804 0.2509804 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.015625 0.015625 0.015625 12 | Ns 0 13 | newmtl Part__Feature001_010_001_002 14 | Ka 0 0 0 15 | Kd 1 1 1 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.015625 0.015625 0.015625 20 | Ns 0 21 | newmtl Part__Feature002_007_001_002 22 | Ka 0 0 0 23 | Kd 1 1 1 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.015625 0.015625 0.015625 28 | Ns 0 29 | newmtl Part__Feature003_004_001_002 30 | Ka 0 0 0 31 | Kd 1 1 1 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.015625 0.015625 0.015625 36 | Ns 0 37 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link4/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:30:31 4 | 5 | newmtl Part__Feature_002_003_001 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.015625 0.015625 0.015625 12 | Ns 0 13 | newmtl Part__Feature002_001_003_001 14 | Ka 0 0 0 15 | Kd 0.2509804 0.2509804 0.2509804 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.015625 0.015625 0.015625 20 | Ns 0 21 | newmtl Part__Feature003_001_003_001 22 | Ka 0 0 0 23 | Kd 1 1 1 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.015625 0.015625 0.015625 28 | Ns 0 29 | newmtl Part__Feature001_001_003_001 30 | Ka 0 0 0 31 | Kd 1 1 1 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.015625 0.015625 0.015625 36 | Ns 0 37 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link5/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:30:10 4 | 5 | newmtl Part__Feature_002_004_003 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.03125 0.03125 0.03125 12 | Ns 0 13 | newmtl Shell_001_001_003 14 | Ka 0 0 0 15 | Kd 1 1 1 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.03125 0.03125 0.03125 20 | Ns 0 21 | newmtl Shell001_001_001_003 22 | Ka 0 0 0 23 | Kd 0.25 0.25 0.25 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.03125 0.03125 0.03125 28 | Ns 0 29 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link6/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:29:58 4 | 5 | newmtl Shell012_002_002_001 6 | Ka 0 0 0 7 | Kd 1 1 1 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.0078125 0.0078125 0.0078125 12 | Ns 0 13 | newmtl Part__Feature001_009_001_002_001 14 | Ka 0 0 0 15 | Kd 0.2509804 0.2509804 0.2509804 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.0078125 0.0078125 0.0078125 20 | Ns 0 21 | newmtl Part__Feature002_006_001_002_001 22 | Ka 0 0 0 23 | Kd 0.2509804 0.2509804 0.2509804 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.0078125 0.0078125 0.0078125 28 | Ns 0 29 | newmtl Face064_002_001_002_001 30 | Ka 0 0 0 31 | Kd 1 0 0 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.0078125 0.0078125 0.0078125 36 | Ns 0 37 | newmtl Face065_002_001_002_001 38 | Ka 0 0 0 39 | Kd 0 1 0 40 | d 1 41 | Tr 0 42 | illum 2 43 | Ks 0.0078125 0.0078125 0.0078125 44 | Ns 0 45 | newmtl Face374_002_001_002_001 46 | Ka 0 0 0 47 | Kd 1 1 1 48 | d 1 49 | Tr 0 50 | illum 2 51 | Ks 0.0078125 0.0078125 0.0078125 52 | Ns 0 53 | newmtl Shell_003_001_002_001 54 | Ka 0 0 0 55 | Kd 0.2509804 0.2509804 0.2509804 56 | d 1 57 | Tr 0 58 | illum 2 59 | Ks 0.0078125 0.0078125 0.0078125 60 | Ns 0 61 | newmtl Face539_002_001_002_001 62 | Ka 0 0 0 63 | Kd 0.2509804 0.2509804 0.2509804 64 | d 1 65 | Tr 0 66 | illum 2 67 | Ks 0.0078125 0.0078125 0.0078125 68 | Ns 0 69 | newmtl Union_001_001_002_001 70 | Ka 0 0 0 71 | Kd 0.03921568999999999 0.54117649999999992 0.7803922 72 | d 1 73 | Tr 0 74 | illum 2 75 | Ks 0.0078125 0.0078125 0.0078125 76 | Ns 0 77 | newmtl Union001_001_001_002_001 78 | Ka 0 0 0 79 | Kd 0.03921568999999999 0.54117649999999992 0.7803922 80 | d 1 81 | Tr 0 82 | illum 2 83 | Ks 0.0078125 0.0078125 0.0078125 84 | Ns 0 85 | newmtl Shell002_002_001_002_001 86 | Ka 0 0 0 87 | Kd 1 1 1 88 | d 1 89 | Tr 0 90 | illum 2 91 | Ks 0.0078125 0.0078125 0.0078125 92 | Ns 0 93 | newmtl Shell003_002_001_002_001 94 | Ka 0 0 0 95 | Kd 1 1 1 96 | d 1 97 | Tr 0 98 | illum 2 99 | Ks 0.0078125 0.0078125 0.0078125 100 | Ns 0 101 | newmtl Shell004_001_001_002_001 102 | Ka 0 0 0 103 | Kd 1 1 1 104 | d 1 105 | Tr 0 106 | illum 2 107 | Ks 0.0078125 0.0078125 0.0078125 108 | Ns 0 109 | newmtl Shell005_001_001_002_001 110 | Ka 0 0 0 111 | Kd 1 1 1 112 | d 1 113 | Tr 0 114 | illum 2 115 | Ks 0.0078125 0.0078125 0.0078125 116 | Ns 0 117 | newmtl Shell007_002_002_001 118 | Ka 0 0 0 119 | Kd 0.25 0.25 0.25 120 | d 1 121 | Tr 0 122 | illum 2 123 | Ks 0.0078125 0.0078125 0.0078125 124 | Ns 0 125 | newmtl Shell011_002_002_001 126 | Ka 0 0 0 127 | Kd 1 1 1 128 | d 1 129 | Tr 0 130 | illum 2 131 | Ks 0.0078125 0.0078125 0.0078125 132 | Ns 0 133 | newmtl Shell006_003_002_001 134 | Ka 0 0 0 135 | Kd 0.9019608 0.9215686 0.9294118 136 | d 1 137 | Tr 0 138 | illum 2 139 | Ks 0.03125 0.03125 0.03125 140 | Ns 0 141 | -------------------------------------------------------------------------------- /urdfs/Panda/meshes/visual/link7/material.lib: -------------------------------------------------------------------------------- 1 | # Aspose.3D Wavefront OBJ Exporter 2 | # Copyright 2004-2022 Aspose Pty Ltd. 3 | # File created: 08/01/2022 23:28:53 4 | 5 | newmtl Part__Mirroring_004_001 6 | Ka 0 0 0 7 | Kd 0.8980392 0.9176471 0.9294118 8 | d 1 9 | Tr 0 10 | illum 2 11 | Ks 0.0625 0.0625 0.0625 12 | Ns 0 13 | newmtl Part__Mirroring002_004_001 14 | Ka 0 0 0 15 | Kd 0.2509804 0.2509804 0.2509804 16 | d 1 17 | Tr 0 18 | illum 2 19 | Ks 0.0625 0.0625 0.0625 20 | Ns 0 21 | newmtl Part__Mirroring003_004_001 22 | Ka 0 0 0 23 | Kd 0.2509804 0.2509804 0.2509804 24 | d 1 25 | Tr 0 26 | illum 2 27 | Ks 0.0625 0.0625 0.0625 28 | Ns 0 29 | newmtl Part__Mirroring005_004_001 30 | Ka 0 0 0 31 | Kd 0.2509804 0.2509804 0.2509804 32 | d 1 33 | Tr 0 34 | illum 2 35 | Ks 0.0625 0.0625 0.0625 36 | Ns 0 37 | newmtl Part__Mirroring006_004_001 38 | Ka 0 0 0 39 | Kd 0.2509804 0.2509804 0.2509804 40 | d 1 41 | Tr 0 42 | illum 2 43 | Ks 0.0625 0.0625 0.0625 44 | Ns 0 45 | newmtl Part__Mirroring007_004_001 46 | Ka 0 0 0 47 | Kd 0.2509804 0.2509804 0.2509804 48 | d 1 49 | Tr 0 50 | illum 2 51 | Ks 0.0625 0.0625 0.0625 52 | Ns 0 53 | newmtl Part__Mirroring001_004_002 54 | Ka 0 0 0 55 | Kd 0.2509804 0.2509804 0.2509804 56 | d 1 57 | Tr 0 58 | illum 2 59 | Ks 0.03125 0.03125 0.03125 60 | Ns 0 61 | newmtl Part__Mirroring004_004_002 62 | Ka 0 0 0 63 | Kd 1 1 1 64 | d 1 65 | Tr 0 66 | illum 2 67 | Ks 0.0625 0.0625 0.0625 68 | Ns 0 69 | -------------------------------------------------------------------------------- /urdfs/Panda/panda.urdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | -------------------------------------------------------------------------------- /urdfs/Panda/panda_dae.urdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | 248 | 249 | 250 | -------------------------------------------------------------------------------- /urdfs/Panda/panda_obj.urdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 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