├── AugDemos ├── README.md ├── aug_dataset_v1.py ├── aug_dataset_v2.py ├── infer_trt.py ├── materials │ ├── aug_pcd_drawer_pcds.gif │ ├── aug_pcd_drawer_pcds.mp4 │ ├── aug_pcd_drawer_rgbs.jpg │ ├── aug_pcd_pouring_pcds.gif │ ├── aug_pcd_pouring_pcds.mp4 │ ├── aug_pcd_pouring_rgbs.jpg │ ├── aug_pcd_uncover_pcds.gif │ ├── aug_pcd_uncover_pcds.mp4 │ ├── aug_pcd_uncover_rgbs.jpg │ ├── objects_point_clouds.jpg │ ├── paired_raw_images.jpg │ ├── raw_images_states.jpg │ └── raw_segmented_objs.jpg └── utils_igev.py ├── BiDP ├── README.md ├── infer.py ├── materials │ ├── BiDP_infer_demo1_drawer_noaug.gif │ ├── BiDP_infer_demo1_drawer_withaug.gif │ ├── BiDP_infer_demo1_openbox_noaug.gif │ ├── BiDP_infer_demo1_openbox_withaug.gif │ ├── BiDP_infer_demo1_pouring_noaug.gif │ ├── BiDP_infer_demo1_pouring_withaug.gif │ ├── BiDP_infer_demo1_uncover_noaug.gif │ ├── BiDP_infer_demo1_uncover_withaug.gif │ ├── BiDP_infer_demo1_unscrew_noaug.gif │ └── BiDP_infer_demo1_unscrew_withaug.gif ├── policies │ ├── agents │ │ ├── dp_agent.py │ │ ├── dp_policy.py │ │ ├── equibot_agent.py │ │ └── equibot_policy.py │ ├── configs │ │ ├── base.yaml │ │ └── basic.yaml │ ├── datasets │ │ ├── dataset.py │ │ └── dataset_loader.py │ ├── utils │ │ ├── diffusion │ │ │ ├── conditional_unet1d.py │ │ │ ├── conv1d_components.py │ │ │ ├── ema_model.py │ │ │ ├── lr_scheduler.py │ │ │ ├── positional_embedding.py │ │ │ └── resnet_with_gn.py │ │ ├── equivariant_diffusion │ │ │ ├── conditional_unet1d.py │ │ │ └── conv1d_components.py │ │ ├── media.py │ │ ├── misc.py │ │ └── norm.py │ └── vision │ │ ├── misc.py │ │ ├── pointnet_encoder.py │ │ ├── sim3_encoder.py │ │ ├── vec_layers.py │ │ └── vec_pointnet.py ├── run_noaug.sh ├── run_withaug.sh ├── test │ ├── drawer │ │ ├── demo_notrans_noaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ └── temp_0021.jpg │ │ └── demo_notrans_withaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ └── temp_0021.jpg │ ├── openbox │ │ ├── demo_notrans_noaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ ├── temp_0023.jpg │ │ │ ├── temp_0024.jpg │ │ │ ├── temp_0025.jpg │ │ │ ├── temp_0026.jpg │ │ │ ├── temp_0027.jpg │ │ │ ├── temp_0028.jpg │ │ │ ├── temp_0029.jpg │ │ │ ├── temp_0030.jpg │ │ │ ├── temp_0031.jpg │ │ │ ├── temp_0032.jpg │ │ │ ├── temp_0033.jpg │ │ │ ├── temp_0034.jpg │ │ │ ├── temp_0035.jpg │ │ │ ├── temp_0036.jpg │ │ │ ├── temp_0037.jpg │ │ │ ├── temp_0038.jpg │ │ │ ├── temp_0039.jpg │ │ │ ├── temp_0040.jpg │ │ │ ├── temp_0041.jpg │ │ │ ├── temp_0042.jpg │ │ │ ├── temp_0043.jpg │ │ │ ├── temp_0044.jpg │ │ │ ├── temp_0045.jpg │ │ │ ├── temp_0046.jpg │ │ │ ├── temp_0047.jpg │ │ │ ├── temp_0048.jpg │ │ │ ├── temp_0049.jpg │ │ │ ├── temp_0050.jpg │ │ │ ├── temp_0051.jpg │ │ │ ├── temp_0052.jpg │ │ │ ├── temp_0053.jpg │ │ │ ├── temp_0054.jpg │ │ │ ├── temp_0055.jpg │ │ │ ├── temp_0056.jpg │ │ │ ├── temp_0057.jpg │ │ │ ├── temp_0058.jpg │ │ │ ├── temp_0059.jpg │ │ │ ├── temp_0060.jpg │ │ │ ├── temp_0061.jpg │ │ │ ├── temp_0062.jpg │ │ │ ├── temp_0063.jpg │ │ │ ├── temp_0064.jpg │ │ │ └── temp_0065.jpg │ │ └── demo_notrans_withaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ ├── temp_0023.jpg │ │ │ ├── temp_0024.jpg │ │ │ ├── temp_0025.jpg │ │ │ ├── temp_0026.jpg │ │ │ ├── temp_0027.jpg │ │ │ ├── temp_0028.jpg │ │ │ ├── temp_0029.jpg │ │ │ ├── temp_0030.jpg │ │ │ ├── temp_0031.jpg │ │ │ ├── temp_0032.jpg │ │ │ ├── temp_0033.jpg │ │ │ ├── temp_0034.jpg │ │ │ ├── temp_0035.jpg │ │ │ ├── temp_0036.jpg │ │ │ ├── temp_0037.jpg │ │ │ ├── temp_0038.jpg │ │ │ ├── temp_0039.jpg │ │ │ ├── temp_0040.jpg │ │ │ ├── temp_0041.jpg │ │ │ ├── temp_0042.jpg │ │ │ ├── temp_0043.jpg │ │ │ ├── temp_0044.jpg │ │ │ ├── temp_0045.jpg │ │ │ ├── temp_0046.jpg │ │ │ ├── temp_0047.jpg │ │ │ ├── temp_0048.jpg │ │ │ ├── temp_0049.jpg │ │ │ ├── temp_0050.jpg │ │ │ ├── temp_0051.jpg │ │ │ ├── temp_0052.jpg │ │ │ ├── temp_0053.jpg │ │ │ ├── temp_0054.jpg │ │ │ ├── temp_0055.jpg │ │ │ ├── temp_0056.jpg │ │ │ ├── temp_0057.jpg │ │ │ ├── temp_0058.jpg │ │ │ ├── temp_0059.jpg │ │ │ ├── temp_0060.jpg │ │ │ ├── temp_0061.jpg │ │ │ ├── temp_0062.jpg │ │ │ ├── temp_0063.jpg │ │ │ ├── temp_0064.jpg │ │ │ └── temp_0065.jpg │ ├── platform_pcd.ply │ ├── pouring │ │ ├── demo_notrans_noaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ └── temp_0023.jpg │ │ └── demo_notrans_withaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ └── temp_0023.jpg │ ├── uncover │ │ ├── demo_notrans_noaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ ├── temp_0023.jpg │ │ │ ├── temp_0024.jpg │ │ │ ├── temp_0025.jpg │ │ │ ├── temp_0026.jpg │ │ │ ├── temp_0027.jpg │ │ │ ├── temp_0028.jpg │ │ │ ├── temp_0029.jpg │ │ │ ├── temp_0030.jpg │ │ │ ├── temp_0031.jpg │ │ │ ├── temp_0032.jpg │ │ │ ├── temp_0033.jpg │ │ │ ├── temp_0034.jpg │ │ │ ├── temp_0035.jpg │ │ │ ├── temp_0036.jpg │ │ │ ├── temp_0037.jpg │ │ │ ├── temp_0038.jpg │ │ │ ├── temp_0039.jpg │ │ │ ├── temp_0040.jpg │ │ │ ├── temp_0041.jpg │ │ │ ├── temp_0042.jpg │ │ │ ├── temp_0043.jpg │ │ │ ├── temp_0044.jpg │ │ │ ├── temp_0045.jpg │ │ │ ├── temp_0046.jpg │ │ │ ├── temp_0047.jpg │ │ │ ├── temp_0048.jpg │ │ │ └── temp_0049.jpg │ │ └── demo_notrans_withaug_id │ │ │ ├── BiDP_infer_demo1.mp4 │ │ │ ├── pred_actions.npy │ │ │ ├── temp_0000.jpg │ │ │ ├── temp_0001.jpg │ │ │ ├── temp_0002.jpg │ │ │ ├── temp_0003.jpg │ │ │ ├── temp_0004.jpg │ │ │ ├── temp_0005.jpg │ │ │ ├── temp_0006.jpg │ │ │ ├── temp_0007.jpg │ │ │ ├── temp_0008.jpg │ │ │ ├── temp_0009.jpg │ │ │ ├── temp_0010.jpg │ │ │ ├── temp_0011.jpg │ │ │ ├── temp_0012.jpg │ │ │ ├── temp_0013.jpg │ │ │ ├── temp_0014.jpg │ │ │ ├── temp_0015.jpg │ │ │ ├── temp_0016.jpg │ │ │ ├── temp_0017.jpg │ │ │ ├── temp_0018.jpg │ │ │ ├── temp_0019.jpg │ │ │ ├── temp_0020.jpg │ │ │ ├── temp_0021.jpg │ │ │ ├── temp_0022.jpg │ │ │ ├── temp_0023.jpg │ │ │ ├── temp_0024.jpg │ │ │ ├── temp_0025.jpg │ │ │ ├── temp_0026.jpg │ │ │ ├── temp_0027.jpg │ │ │ ├── temp_0028.jpg │ │ │ ├── temp_0029.jpg │ │ │ ├── temp_0030.jpg │ │ │ ├── temp_0031.jpg │ │ │ ├── temp_0032.jpg │ │ │ ├── temp_0033.jpg │ │ │ ├── temp_0034.jpg │ │ │ ├── temp_0035.jpg │ │ │ ├── temp_0036.jpg │ │ │ ├── temp_0037.jpg │ │ │ ├── temp_0038.jpg │ │ │ ├── temp_0039.jpg │ │ │ ├── temp_0040.jpg │ │ │ ├── temp_0041.jpg │ │ │ ├── temp_0042.jpg │ │ │ ├── temp_0043.jpg │ │ │ ├── temp_0044.jpg │ │ │ ├── temp_0045.jpg │ │ │ ├── temp_0046.jpg │ │ │ ├── temp_0047.jpg │ │ │ ├── temp_0048.jpg │ │ │ └── temp_0049.jpg │ └── unscrew │ │ ├── demo_notrans_noaug_id │ │ ├── BiDP_infer_demo1.mp4 │ │ ├── pred_actions.npy │ │ ├── temp_0000.jpg │ │ ├── temp_0001.jpg │ │ ├── temp_0002.jpg │ │ ├── temp_0003.jpg │ │ ├── temp_0004.jpg │ │ ├── temp_0005.jpg │ │ ├── temp_0006.jpg │ │ ├── temp_0007.jpg │ │ ├── temp_0008.jpg │ │ ├── temp_0009.jpg │ │ ├── temp_0010.jpg │ │ ├── temp_0011.jpg │ │ ├── temp_0012.jpg │ │ ├── temp_0013.jpg │ │ ├── temp_0014.jpg │ │ ├── temp_0015.jpg │ │ ├── temp_0016.jpg │ │ ├── temp_0017.jpg │ │ ├── temp_0018.jpg │ │ ├── temp_0019.jpg │ │ ├── temp_0020.jpg │ │ ├── temp_0021.jpg │ │ ├── temp_0022.jpg │ │ ├── temp_0023.jpg │ │ ├── temp_0024.jpg │ │ └── temp_0025.jpg │ │ └── demo_notrans_withaug_id │ │ ├── BiDP_infer_demo1.mp4 │ │ ├── pred_actions.npy │ │ ├── temp_0000.jpg │ │ ├── temp_0001.jpg │ │ ├── temp_0002.jpg │ │ ├── temp_0003.jpg │ │ ├── temp_0004.jpg │ │ ├── temp_0005.jpg │ │ ├── temp_0006.jpg │ │ ├── temp_0007.jpg │ │ ├── temp_0008.jpg │ │ ├── temp_0009.jpg │ │ ├── temp_0010.jpg │ │ ├── temp_0011.jpg │ │ ├── temp_0012.jpg │ │ ├── temp_0013.jpg │ │ ├── temp_0014.jpg │ │ ├── temp_0015.jpg │ │ ├── temp_0016.jpg │ │ ├── temp_0017.jpg │ │ ├── temp_0018.jpg │ │ ├── temp_0019.jpg │ │ ├── temp_0020.jpg │ │ ├── temp_0021.jpg │ │ ├── temp_0022.jpg │ │ ├── temp_0023.jpg │ │ ├── temp_0024.jpg │ │ └── temp_0025.jpg └── train.py └── README.md /AugDemos/README.md: -------------------------------------------------------------------------------- 1 | # Augmentation of Demonstrations 2 | In this part, we show how to condcut the point cloud-level geometry augmentation of manipulated objects for all valid demonstrations in each task. These pre-processed data will be used for training BiDP models. 3 | 4 | ## Step 0: Data download and environment configuration 5 | 6 | * Download the raw binocular paired left/right images (in *.jpg format) and the corresponding dual robot arm states (in *.json format) for each task. We have uploaded them (`drawer.zip`, `pouring.zip`, `unscrew.zip`, `uncover.zip` and `openbox.zip`) on [huggingface/YOTO](https://huggingface.co/HoyerChou/YOTO/tree/main). We also uploaded the corresponding segmented manipulated objects of all left-view images (`drawer_mask.zip`, `pouring_mask.zip`, `unscrew_mask.zip`, `uncover_mask.zip` and `openbox_mask.zip`). These demonstrations are auto-rollout verified in real-world, and will be pre-processed for training. 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
raw images and states raw segmented objects
18 | 19 | * Configure the dependent environment required for binocular stereo matching. We used the [IGEV](https://github.com/gangweiX/IGEV) algorithm and [KingFisher](https://dexforce-3dvision.com/productinfo/1022811.html) binocular camera. Among them, you need to utilize the dependency packages [glia](http://rjyfb:123456@69.235.177.182:10801/simple/glia/) and [glia-trt](http://rjyfb:123456@69.235.177.182:10801/simple/glia-trt/) developed by `DexForce` in file [infer_trt.py](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/infer_trt.py#L12). Despite this, due to hardware differences, readers may need to use commercial binocular cameras (such as RealSense) to collect similar data. 20 | 21 | ## Step 1: Pre-processing of all valid demonstrations 22 | ``` 23 | python aug_dataset_v1.py 24 | ``` 25 | Before running, you can adjust many useful parameters in this script. If you cannot reproduce our environment, you can directly download our pre-processed jsons (`drawer_preprocessed.json`, `pouring_preprocessed.json`, `unscrew_preprocessed.json`, `uncover_preprocessed.json` and `openbox_preprocessed.json`) in [huggingface/YOTO](https://huggingface.co/HoyerChou/YOTO/tree/main) for training. 26 | * [`debug_flag`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v1.py#L216): whether using the debug mode to checking pre-processed results, such as matching effect, object masks and segmented object point clouds. 27 | * [`robot2camera_trans`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v1.py#L217): whether tansforming the dual-arm robot states (6-DoF keyposes) from the robot world into the camera world. We have valided that set it as True will not be better. We thus utilize visual observations in the camera view and robot actions in the robot view to train all policy models. 28 | * [`task_names`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v1.py#L219): You can change the task name list for pre-processing. We currently collected a fixed number of demonstrations through *Auto-Rollout Verification in Real-World* for each task (`drawer`, `pouring`, `unscrew`, `uncover`, `openbox`), which are `243/162/54/45/36` respectively. 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 |
matched binocular images objects point clouds
40 | 41 | ## Step 2: Geometric transformation in point cloud-level 42 | ``` 43 | python aug_dataset_v2.py 44 | ``` 45 | Before running, you can also adjust many useful parameters in this script. We have also uploaded our pre-processed jsons (`drawer_preprocessed_aug100x_*.json`, `pouring_preprocessed_aug100x_*.json`, `unscrew_preprocessed_aug100x.json`, `uncover_preprocessed_aug100x.json` and `openbox_preprocessed_aug100x.json`) in [huggingface/YOTO](https://huggingface.co/HoyerChou/YOTO/tree/main) for training. 46 | * [`debug_flag`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v2.py#L431): whether using the debug mode to checking pre-processed results, such as matching effect, object masks and segmented object point clouds. 47 | * [`aug_times`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v2.py#L432): set the augmentation times for each single demonstration. We can set it as `1 / 5 / 25 / 100 / 500`. And we have valided that set it as `100` is good enough. 48 | * [`left_pts_num`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v2.py#L433): set the left points number in manipulated object point clouds. We can set it as `32768 / 16384 / 8192 / 4096 / 2048`. In fact, we don’t need to set it too large, because the number of points when sampling objects during training is generally `1024 or 2048`. We set its as `4096` in our 100x augmentation for saving space of final pre-processed results. 49 | * [`task_names`](https://github.com/hnuzhy/YOTO/blob/main/AugDemos/aug_dataset_v2.py#L434): You can change the task name list for pre-processing. We currently generated 100x demonstrations through *Geometric Transformation of Point Clouds* for each task (`drawer`, `pouring`, `unscrew`, `uncover`, `openbox`), which are `24300/16200/5400/4500/3600` respectively. 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 |
the matched binocular images augmented objects point clouds
69 | 70 | -------------------------------------------------------------------------------- /AugDemos/infer_trt.py: -------------------------------------------------------------------------------- 1 | import os 2 | import torch 3 | import argparse 4 | import time 5 | import cv2 6 | import yaml 7 | import open3d as o3d 8 | import numpy as np 9 | from glob import glob 10 | 11 | from utils_igev import StereoRectifyUtil 12 | from glia.dl.pipelines import StereoInterface 13 | 14 | 15 | def generate_pcd(left_image, disp, fx, fy, cx, cy, baseline, object_mask_binary=None, roi_mask_binary=None): 16 | height, width, _ = left_image.shape 17 | depth = fx * baseline / disp 18 | 19 | left_img_o3d = o3d.geometry.Image(left_image.astype(np.uint8)) 20 | intrinsic = o3d.camera.PinholeCameraIntrinsic(width, height, fx, fy, cx, cy) 21 | depth_img_o3d = o3d.geometry.Image(depth.astype(np.float32)) 22 | rgbd_img = o3d.geometry.RGBDImage.create_from_color_and_depth( 23 | left_img_o3d, 24 | depth_img_o3d, 25 | depth_scale=1.0, 26 | depth_trunc=depth.max()+1, # do not remove 3d point in any pixel 27 | # depth_trunc=4.0, 28 | convert_rgb_to_intensity=False, 29 | ) 30 | pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_img, intrinsic) 31 | print(len(pcd.points), np.array(pcd.points).shape, height*width) 32 | 33 | if object_mask_binary is not None: 34 | # https://www.cnblogs.com/massquantity/p/8908859.html 35 | left_pts_idx = np.where(object_mask_binary.reshape(-1) > 0)[0] 36 | print("left points:", len(left_pts_idx)) 37 | pcd = pcd.select_by_index(left_pts_idx) 38 | 39 | if roi_mask_binary is not None: 40 | left_pts_idx = np.where(roi_mask_binary.reshape(-1) > 0)[0] 41 | print("left points:", len(left_pts_idx)) 42 | pcd = pcd.select_by_index(left_pts_idx) 43 | 44 | return pcd, depth 45 | 46 | 47 | def init_igev_model(): 48 | model_path = "./kingfisher/igev.trt" 49 | calib_file = "./kingfisher/calib_1106.yaml" 50 | im_height, im_width = 1024, 1280 51 | 52 | # IGEV Tensorrt模型初始化成功 53 | model = StereoInterface(backend="trt", model_path=model_path) 54 | model.cuda() 55 | 56 | # 加载相机参数文件 57 | with open(calib_file, "r", encoding="utf-8") as f: 58 | stereo_res = yaml.load(stream=f, Loader=yaml.FullLoader) 59 | ret_results = StereoRectifyUtil.rectify_params(stereo_res, im_height, im_width) 60 | ori_k1, R1, rect_k1, baseline, map_x_1, map_y_1, map_x_2, map_y_2 = ret_results 61 | 62 | model.set_camera(0, im_height, im_width, 63 | fx=rect_k1[0, 0], fy=rect_k1[1, 1], cx=rect_k1[0, 2], cy=rect_k1[1, 2]) 64 | model.set_camera(1, im_height, im_width, 65 | fx=rect_k1[0, 0], fy=rect_k1[1, 1], cx=rect_k1[0, 2], cy=rect_k1[1, 2]) 66 | 67 | param_list = [model, R1, rect_k1, baseline, map_x_1, map_y_1, map_x_2, map_y_2] 68 | return param_list 69 | 70 | 71 | if __name__ == '__main__': 72 | 73 | print("infer scene point cloud using kingfisher and igev!!!") 74 | -------------------------------------------------------------------------------- /AugDemos/materials/aug_pcd_drawer_pcds.gif: 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np.array(stereo_res["cam2_k"]) 10 | 11 | cam1_dist = np.array(stereo_res["dist_1"]).reshape(-1) 12 | cam2_dist = np.array(stereo_res["dist_2"]).reshape(-1) 13 | R = np.array(stereo_res["R_l_r"]) 14 | t = np.array(stereo_res["t_l_r"]) 15 | 16 | # 注意后端不再考虑畸变，需前端确认已进行去畸变处理。 17 | R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify( 18 | cameraMatrix1=cam1_K, 19 | distCoeffs1=cam1_dist, 20 | cameraMatrix2=cam2_K, 21 | distCoeffs2=cam2_dist, 22 | imageSize=(width, height), 23 | R=R, 24 | T=t, 25 | flags=1024, 26 | newImageSize=(0, 0), 27 | ) 28 | 29 | rectified_image_size = (width, height) 30 | 31 | map_x_1, map_y_1 = cv2.initUndistortRectifyMap( 32 | cameraMatrix=cam1_K, 33 | distCoeffs=np.zeros((1, 5)), 34 | R=R1, 35 | newCameraMatrix=P1, 36 | size=rectified_image_size, 37 | m1type=cv2.CV_32FC1, 38 | ) 39 | map_x_2, map_y_2 = cv2.initUndistortRectifyMap( 40 | cameraMatrix=cam2_K, 41 | distCoeffs=np.zeros((1, 5)), 42 | R=R2, 43 | newCameraMatrix=P2, 44 | size=rectified_image_size, 45 | m1type=cv2.CV_32FC1, 46 | ) 47 | 48 | rect_cam_k = P2[:3, :3] 49 | baseline = -P2[0, 3] / P2[0, 0] * 0.001 50 | 51 | return ( 52 | cam1_K, 53 | R1, 54 | rect_cam_k, 55 | baseline, 56 | map_x_1, 57 | map_y_1, 58 | map_x_2, 59 | map_y_2, 60 | ) 61 | 62 | @staticmethod 63 | def rectify_img(image: np.ndarray, map_x: np.ndarray, map_y: np.ndarray): 64 | return cv2.remap(image, map_x, map_y, interpolation=cv2.INTER_LINEAR) 65 | 66 | @staticmethod 67 | def visualize_rect_imgs(left_img: np.ndarray, right_img: np.ndarray, save_path: str): 68 | height, width, _ = left_img.shape 69 | img = np.hstack((left_img, right_img)) 70 | iend = 8 71 | for i in range(1, iend + 1): 72 | h = int(height/iend * i) 73 | img = cv2.line(img, (0, h), (width*2, h), (0,0,255), 2) 74 | cv2.imwrite(save_path, img) 75 | 76 | @staticmethod 77 | def from_rectify_pcd_to_origin_depth( 78 | left_img: np.ndarray, 79 | src_pcd: o3d.geometry.PointCloud, 80 | source2target_rotation: np.ndarray, 81 | target_camera_intrinsic: np.ndarray, 82 | img_h: int, 83 | img_w: int, 84 | depth_scale: float = 1.0, 85 | depth_trunc: float = 4.0, 86 | ): 87 | # 1. get 3xN vertices coordinate of source pcd 88 | source_point_cloud = np.asarray(src_pcd.points).T 89 | 90 | # 2. point cloud coordinate transformation 91 | target_point_cloud = source2target_rotation.dot(source_point_cloud) 92 | 93 | assert (target_point_cloud[2] > 0).all() 94 | return target_point_cloud 95 | 96 | # 3. target point cloud reproject to image 97 | target_depth_map = np.zeros((img_h, img_w), dtype=np.float32) 98 | target_uv = np.around(np.dot(target_camera_intrinsic, target_point_cloud) / target_point_cloud[2, :]) 99 | 100 | # 交换前两列 101 | target_uv[2, :] = target_uv[0, :] 102 | target_uv = target_uv[1:, :] 103 | 104 | val_locs = ( 105 | (target_uv[0] < img_h) 106 | & (target_uv[0] >= 0) 107 | & (target_uv[1] < img_w) 108 | & (target_uv[1] >= 0) 109 | ) 110 | 111 | # 图像平面内的有效x,y位置 112 | row_col_pos = target_uv.T[val_locs].astype(np.int32) 113 | # 图像平面内的有效z位置 114 | target_pt_zs = target_point_cloud[2][val_locs] 115 | 116 | # 按深度值从大到小排序后，依次投影到目标图像平面，最终同像素位置小深度值会替换大深度值 117 | z_sorted_idxs = np.argsort(target_pt_zs) 118 | z_sorted_idxs = z_sorted_idxs[::-1] 119 | 120 | row_col_pos = row_col_pos[z_sorted_idxs] 121 | target_depth_map[row_col_pos[:, 0], row_col_pos[:, 1]] = target_pt_zs[z_sorted_idxs] 122 | 123 | # 4. return target depth map and pcd 124 | height, width, _ = left_img.shape 125 | fx = target_camera_intrinsic[0, 0] 126 | fy = target_camera_intrinsic[1, 1] 127 | cx = target_camera_intrinsic[0, 2] 128 | cy = target_camera_intrinsic[1, 2] 129 | left_img_o3d = o3d.geometry.Image((left_img).astype(np.uint8)) 130 | intrinsic = o3d.camera.PinholeCameraIntrinsic(width, height, fx, fy, cx, cy) 131 | depth_img_o3d = o3d.geometry.Image(target_depth_map.astype(np.float32)) 132 | rgbd_img = o3d.geometry.RGBDImage.create_from_color_and_depth( 133 | left_img_o3d, 134 | depth_img_o3d, 135 | depth_scale=depth_scale, 136 | depth_trunc=depth_trunc, 137 | convert_rgb_to_intensity=False, 138 | ) 139 | 140 | tgt_pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_img, intrinsic) 141 | 142 | return target_depth_map, tgt_pcd 143 | -------------------------------------------------------------------------------- /BiDP/materials/BiDP_infer_demo1_drawer_noaug.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hnuzhy/YOTO/3fe6edc9a4bc843a54ae53210704a08b962aa8ad/BiDP/materials/BiDP_infer_demo1_drawer_noaug.gif -------------------------------------------------------------------------------- /BiDP/materials/BiDP_infer_demo1_drawer_withaug.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hnuzhy/YOTO/3fe6edc9a4bc843a54ae53210704a08b962aa8ad/BiDP/materials/BiDP_infer_demo1_drawer_withaug.gif 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-------------------------------------------------------------------------------- /BiDP/policies/agents/dp_policy.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import hydra 3 | import torch 4 | from torch import nn 5 | 6 | from policies.vision.pointnet_encoder import PointNetEncoder 7 | from policies.utils.diffusion.ema_model import EMAModel 8 | from policies.utils.diffusion.conditional_unet1d import ConditionalUnet1D 9 | from policies.utils.diffusion.resnet_with_gn import get_resnet, replace_bn_with_gn 10 | 11 | 12 | class DPPolicy(nn.Module): 13 | def __init__(self, cfg, device="cpu"): 14 | super().__init__() 15 | self.cfg = cfg 16 | self.hidden_dim = hidden_dim = cfg.model.hidden_dim 17 | self.obs_mode = cfg.model.obs_mode 18 | self.device = device 19 | 20 | # |o|o| observations: 2 21 | # | |a|a|a|a|a|a|a|a| actions executed: 8 22 | # |p|p|p|p|p|p|p|p|p|p|p|p|p|p|p|p| actions predicted: 16 23 | self.pred_horizon = cfg.model.pred_horizon 24 | self.obs_horizon = cfg.model.obs_horizon 25 | self.action_horizon = cfg.model.ac_horizon 26 | 27 | if hasattr(cfg.model, "num_diffusion_iters"): 28 | self.num_diffusion_iters = cfg.model.num_diffusion_iters 29 | else: 30 | self.num_diffusion_iters = cfg.model.noise_scheduler.num_train_timesteps 31 | 32 | self.num_eef = cfg.env.num_eef 33 | self.eef_dim = cfg.env.eef_dim 34 | self.dof = cfg.env.dof 35 | if cfg.model.obs_mode == "state": 36 | self.obs_dim = self.num_eef * self.eef_dim 37 | elif cfg.model.obs_mode == "rgb": 38 | self.obs_dim = 512 + self.num_eef * self.eef_dim 39 | else: 40 | self.obs_dim = hidden_dim + self.num_eef * self.eef_dim 41 | self.action_dim = self.dof * cfg.env.num_eef 42 | 43 | if self.obs_mode.startswith("pc"): 44 | self.encoder = PointNetEncoder( 45 | h_dim=hidden_dim, 46 | c_dim=hidden_dim, 47 | num_layers=cfg.model.encoder.backbone_args.num_layers, 48 | ) 49 | elif self.obs_mode == "rgb": 50 | self.encoder = replace_bn_with_gn(get_resnet("resnet18")) 51 | else: 52 | self.encoder = nn.Identity() 53 | self.noise_pred_net = ConditionalUnet1D( 54 | input_dim=self.action_dim, 55 | diffusion_step_embed_dim=self.obs_dim * self.obs_horizon, 56 | global_cond_dim=self.obs_dim * self.obs_horizon, 57 | ) 58 | 59 | self.nets = nn.ModuleDict( 60 | {"encoder": self.encoder, "noise_pred_net": self.noise_pred_net} 61 | ) 62 | self.ema = EMAModel(model=copy.deepcopy(self.nets), power=0.75) 63 | 64 | self._init_torch_compile() 65 | 66 | self.noise_scheduler = hydra.utils.instantiate(cfg.model.noise_scheduler) 67 | 68 | num_parameters = sum(p.numel() for p in self.parameters() if p.requires_grad) 69 | print(f"Initialized DP Policy with {num_parameters} parameters") 70 | 71 | def _init_torch_compile(self): 72 | if self.cfg.model.use_torch_compile: 73 | self.encoder_handle = torch.compile(self.encoder) 74 | self.noise_pred_net_handle = torch.compile(self.noise_pred_net) 75 | 76 | def forward(self, obs, predict_action=True, debug=False): 77 | # assumes that observation has format: 78 | # - pc: [BS, obs_horizon, num_pts, 3] 79 | # - state: [BS, obs_horizon, obs_dim] 80 | # returns: 81 | # - action: [BS, pred_horizon, ac_dim] 82 | pc = obs["pc"] 83 | state = obs["state"] 84 | 85 | if self.obs_mode.startswith("pc"): 86 | pc = self.pc_normalizer.normalize(pc) 87 | state = self.state_normalizer.normalize(state) 88 | 89 | pc_shape = pc.shape 90 | batch_size = pc.shape[0] 91 | 92 | ema_nets = self.ema.averaged_model 93 | 94 | if self.obs_mode == "state": 95 | z = state 96 | else: 97 | if self.obs_mode == "rgb": 98 | rgb = obs["rgb"] 99 | rgb_shape = rgb.shape 100 | flattened_rgb = rgb.reshape( 101 | batch_size * self.obs_horizon, *rgb_shape[-3:] 102 | ) 103 | z = ema_nets["encoder"](flattened_rgb.permute(0, 3, 1, 2)) 104 | else: 105 | flattened_pc = pc.reshape(batch_size * self.obs_horizon, *pc_shape[-2:]) 106 | z = ema_nets["encoder"](flattened_pc.permute(0, 2, 1))["global"] 107 | z = z.reshape(batch_size, self.obs_horizon, -1) 108 | z = torch.cat([z, state], dim=-1) 109 | obs_cond = z.reshape(batch_size, -1) # (BS, obs_horizon * obs_dim) 110 | 111 | initial_noise_scale = 0.0 if debug else 1.0 112 | noisy_action = ( 113 | torch.randn((batch_size, self.pred_horizon, self.action_dim)).to( 114 | self.device 115 | ) 116 | * initial_noise_scale 117 | ) 118 | curr_action = noisy_action 119 | self.noise_scheduler.set_timesteps(self.num_diffusion_iters) 120 | 121 | for k in self.noise_scheduler.timesteps: 122 | # predict noise 123 | noise_pred = ema_nets["noise_pred_net"]( 124 | sample=curr_action, timestep=k, global_cond=obs_cond 125 | ) 126 | 127 | # inverse diffusion step 128 | curr_action = self.noise_scheduler.step( 129 | model_output=noise_pred, timestep=k, sample=curr_action 130 | ).prev_sample 131 | 132 | ret = dict(ac=curr_action) 133 | return ret 134 | 135 | def step_ema(self): 136 | self.ema.step(self.nets) 137 | -------------------------------------------------------------------------------- /BiDP/policies/configs/base.yaml: -------------------------------------------------------------------------------- 1 | prefix: default 2 | device: cuda 3 | mode: train 4 | log_dir: logs/${mode} 5 | eval_data_path: null 6 | use_wandb: true 7 | seed: 0 8 | 9 | agent: 10 | agent_name: dp 11 | 12 | env: 13 | env_class: ??? 14 | num_eef: 2 15 | dof: 7 16 | eef_dim: 13 17 | vectorize: false 18 | args: 19 | num_eef: ${env.num_eef} 20 | dof: ${env.dof} 21 | seed: ${seed} 22 | obs_mode: "pc" 23 | ac_mode: "rel" 24 | max_episode_length: ??? 25 | num_points: ${data.dataset.num_points} 26 | randomize_rotation: false 27 | randomize_scale: false 28 | scale_low: 1.0 29 | scale_high: 1.0 30 | scale_aspect_limit: 100.0 31 | uniform_scaling: false 32 | 33 | data: 34 | dataset_class: base_dataset 35 | dataset: 36 | num_training_steps: ??? 37 | path: null 38 | num_points: 1024 39 | num_augment: 0 40 | same_aug_per_sample: true 41 | aug_keep_original: true 42 | aug_scale_low: 0.5 43 | aug_scale_high: 1.5 44 | aug_scale_aspect_limit: 1.0 45 | aug_scale_rot: -1 46 | aug_scale_pos: 0.1 47 | aug_zero_z_offset: false 48 | aug_center: [0., 0., 0.] 49 | shuffle_pc: true 50 | num_workers: 12 51 | dof: ${env.dof} 52 | num_eef: ${env.num_eef} 53 | eef_dim: ${env.eef_dim} 54 | obs_horizon: ${model.obs_horizon} 55 | pred_horizon: ${model.pred_horizon} 56 | reduce_horizon_dim: false 57 | min_demo_length: 15 58 | 59 | model: 60 | hidden_dim: 32 61 | noise_scheduler: 62 | _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler 63 | num_train_timesteps: 100 64 | beta_schedule: squaredcos_cap_v2 65 | clip_sample: true 66 | prediction_type: epsilon 67 | obs_horizon: 2 68 | ac_horizon: 8 69 | pred_horizon: 16 70 | encoder: 71 | c_dim: ${model.hidden_dim} 72 | backbone_type: vn_pointnet 73 | backbone_args: 74 | h_dim: ${model.hidden_dim} 75 | c_dim: ${model.encoder.c_dim} 76 | num_layers: 4 77 | knn: 8 78 | obs_mode: ${env.args.obs_mode} 79 | ac_mode: ${env.args.ac_mode} 80 | use_torch_compile: false 81 | 82 | training: 83 | batch_size: 32 84 | num_epochs: 2000 85 | lr: 3e-5 86 | weight_decay: 1e-6 87 | num_eval_episodes: 10 88 | eval_interval: 1000000 # do not eval during training 89 | save_interval: 50 90 | vis_interval: 100 91 | ckpt: null 92 | 93 | eval: 94 | last_ckpt: 1999 95 | num_ckpts_to_eval: 5 96 | 97 | hydra: 98 | run: 99 | dir: ${log_dir}/${prefix} 100 | 101 | wandb: 102 | entity: wandb_zhouhuayi 103 | project: BiDP 104 | -------------------------------------------------------------------------------- /BiDP/policies/configs/basic.yaml: -------------------------------------------------------------------------------- 1 | prefix: default 2 | device: cuda 3 | mode: train 4 | log_dir: logs/${mode} 5 | eval_data_path: null 6 | use_wandb: true 7 | seed: 0 8 | 9 | 10 | 11 | agent: 12 | agent_name: dp # equibot or dp 13 | 14 | env: 15 | env_class: ??? 16 | num_eef: 1 17 | dof: 7 18 | eef_dim: 13 19 | vectorize: false 20 | args: 21 | num_eef: ${env.num_eef} 22 | dof: ${env.dof} 23 | seed: ${seed} 24 | obs_mode: "pc" 25 | ac_mode: "rel" 26 | max_episode_length: 100 27 | num_points: ${data.dataset.num_points} 28 | randomize_rotation: false 29 | randomize_scale: false 30 | scale_low: 1.0 31 | scale_high: 1.0 32 | scale_aspect_limit: 100.0 33 | uniform_scaling: false 34 | cam_resolution: 256 35 | vis: true 36 | freq: 5 37 | ac_noise: 0.0 38 | 39 | data: 40 | dataset_class: base_dataset 41 | dataset: 42 | num_training_steps: ??? 43 | path: null 44 | num_points: 1024 45 | shuffle_pc: true 46 | task_name: ??? 47 | is_augmented: 0 48 | is_transformed: 0 49 | num_workers: 4 50 | dof: ${env.dof} 51 | num_eef: ${env.num_eef} 52 | eef_dim: ${env.eef_dim} 53 | obs_horizon: ${model.obs_horizon} 54 | pred_horizon: ${model.pred_horizon} 55 | reduce_horizon_dim: false 56 | min_demo_length: 15 57 | one_real_data: null 58 | 59 | model: 60 | hidden_dim: 32 61 | noise_scheduler: 62 | _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler 63 | num_train_timesteps: 100 64 | beta_schedule: squaredcos_cap_v2 65 | clip_sample: true 66 | prediction_type: epsilon 67 | obs_horizon: 1 68 | ac_horizon: 8 69 | pred_horizon: 16 70 | encoder: 71 | c_dim: ${model.hidden_dim} 72 | backbone_type: vn_pointnet 73 | backbone_args: 74 | h_dim: ${model.hidden_dim} 75 | c_dim: ${model.encoder.c_dim} 76 | num_layers: 4 77 | knn: 8 78 | obs_mode: ${env.args.obs_mode} 79 | ac_mode: ${env.args.ac_mode} 80 | use_torch_compile: false 81 | 82 | training: 83 | batch_size: 16 84 | num_epochs: 2000 85 | lr: 3e-5 86 | weight_decay: 1e-6 87 | num_eval_episodes: 10 88 | eval_interval: 1000000 # do not eval during training 89 | save_interval: 50 90 | vis_interval: 100 91 | ckpt: null 92 | 93 | eval: 94 | last_ckpt: 1999 95 | num_ckpts_to_eval: 5 96 | 97 | hydra: 98 | run: 99 | dir: ${log_dir}/${prefix} 100 | 101 | wandb: 102 | entity: wandb_zhouhuayi 103 | project: BiDP 104 | -------------------------------------------------------------------------------- /BiDP/policies/utils/diffusion/conv1d_components.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class Downsample1d(nn.Module): 7 | def __init__(self, dim): 8 | super().__init__() 9 | self.conv = nn.Conv1d(dim, dim, 3, 2, 1) 10 | 11 | def forward(self, x): 12 | return self.conv(x) 13 | 14 | 15 | class Upsample1d(nn.Module): 16 | def __init__(self, dim): 17 | super().__init__() 18 | self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1) 19 | 20 | def forward(self, x): 21 | return self.conv(x) 22 | 23 | 24 | class Conv1dBlock(nn.Module): 25 | """ 26 | Conv1d --> GroupNorm --> Mish 27 | """ 28 | 29 | def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8): 30 | super().__init__() 31 | 32 | self.block = nn.Sequential( 33 | nn.Conv1d( 34 | inp_channels, out_channels, kernel_size, padding=kernel_size // 2 35 | ), 36 | # Rearrange('batch channels horizon -> batch channels 1 horizon'), 37 | nn.GroupNorm(n_groups, out_channels), 38 | # Rearrange('batch channels 1 horizon -> batch channels horizon'), 39 | nn.Mish(), 40 | ) 41 | 42 | def forward(self, x): 43 | return self.block(x) 44 | 45 | 46 | def test(): 47 | cb = Conv1dBlock(256, 128, kernel_size=3) 48 | x = torch.zeros((1, 256, 16)) 49 | o = cb(x) 50 | -------------------------------------------------------------------------------- /BiDP/policies/utils/diffusion/ema_model.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import torch 3 | from torch.nn.modules.batchnorm import _BatchNorm 4 | 5 | 6 | class EMAModel: 7 | """ 8 | Exponential Moving Average of models weights 9 | """ 10 | 11 | def __init__( 12 | self, 13 | model, 14 | update_after_step=0, 15 | inv_gamma=1.0, 16 | power=2 / 3, 17 | min_value=0.0, 18 | max_value=0.9999, 19 | ): 20 | """ 21 | @crowsonkb's notes on EMA Warmup: 22 | If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan 23 | to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps), 24 | gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 25 | at 215.4k steps). 26 | Args: 27 | inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. 28 | power (float): Exponential factor of EMA warmup. Default: 2/3. 29 | min_value (float): The minimum EMA decay rate. Default: 0. 30 | """ 31 | 32 | self.averaged_model = model 33 | self.averaged_model.eval() 34 | self.averaged_model.requires_grad_(False) 35 | 36 | self.update_after_step = update_after_step 37 | self.inv_gamma = inv_gamma 38 | self.power = power 39 | self.min_value = min_value 40 | self.max_value = max_value 41 | 42 | self.decay = 0.0 43 | self.optimization_step = 0 44 | 45 | def get_decay(self, optimization_step): 46 | """ 47 | Compute the decay factor for the exponential moving average. 48 | """ 49 | step = max(0, optimization_step - self.update_after_step - 1) 50 | value = 1 - (1 + step / self.inv_gamma) ** -self.power 51 | 52 | if step <= 0: 53 | return 0.0 54 | 55 | return max(self.min_value, min(value, self.max_value)) 56 | 57 | @torch.no_grad() 58 | def step(self, new_model): 59 | self.decay = self.get_decay(self.optimization_step) 60 | 61 | # old_all_dataptrs = set() 62 | # for param in new_model.parameters(): 63 | # data_ptr = param.data_ptr() 64 | # if data_ptr != 0: 65 | # old_all_dataptrs.add(data_ptr) 66 | 67 | all_dataptrs = set() 68 | for module, ema_module in zip( 69 | new_model.modules(), self.averaged_model.modules() 70 | ): 71 | for param, ema_param in zip( 72 | module.parameters(recurse=False), ema_module.parameters(recurse=False) 73 | ): 74 | # iterative over immediate parameters only. 75 | if isinstance(param, dict): 76 | raise RuntimeError("Dict parameter not supported") 77 | 78 | # data_ptr = param.data_ptr() 79 | # if data_ptr != 0: 80 | # all_dataptrs.add(data_ptr) 81 | 82 | if isinstance(module, _BatchNorm): 83 | # skip batchnorms 84 | ema_param.copy_(param.to(dtype=ema_param.dtype).data) 85 | elif not param.requires_grad: 86 | ema_param.copy_(param.to(dtype=ema_param.dtype).data) 87 | else: 88 | ema_param.mul_(self.decay) 89 | ema_param.add_( 90 | param.data.to(dtype=ema_param.dtype), alpha=1 - self.decay 91 | ) 92 | 93 | # verify that iterating over module and then parameters is identical to parameters recursively. 94 | # assert old_all_dataptrs == all_dataptrs 95 | self.optimization_step += 1 96 | -------------------------------------------------------------------------------- /BiDP/policies/utils/diffusion/lr_scheduler.py: -------------------------------------------------------------------------------- 1 | from diffusers.optimization import ( 2 | Union, 3 | SchedulerType, 4 | Optional, 5 | Optimizer, 6 | TYPE_TO_SCHEDULER_FUNCTION, 7 | ) 8 | 9 | 10 | def get_scheduler( 11 | name: Union[str, SchedulerType], 12 | optimizer: Optimizer, 13 | num_warmup_steps: Optional[int] = None, 14 | num_training_steps: Optional[int] = None, 15 | **kwargs, 16 | ): 17 | """ 18 | Added kwargs vs diffuser's original implementation 19 | 20 | Unified API to get any scheduler from its name. 21 | 22 | Args: 23 | name (`str` or `SchedulerType`): 24 | The name of the scheduler to use. 25 | optimizer (`torch.optim.Optimizer`): 26 | The optimizer that will be used during training. 27 | num_warmup_steps (`int`, *optional*): 28 | The number of warmup steps to do. This is not required by all schedulers (hence the argument being 29 | optional), the function will raise an error if it's unset and the scheduler type requires it. 30 | num_training_steps (`int``, *optional*): 31 | The number of training steps to do. This is not required by all schedulers (hence the argument being 32 | optional), the function will raise an error if it's unset and the scheduler type requires it. 33 | """ 34 | name = SchedulerType(name) 35 | schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] 36 | if name == SchedulerType.CONSTANT: 37 | return schedule_func(optimizer, **kwargs) 38 | 39 | # All other schedulers require `num_warmup_steps` 40 | if num_warmup_steps is None: 41 | raise ValueError( 42 | f"{name} requires `num_warmup_steps`, please provide that argument." 43 | ) 44 | 45 | if name == SchedulerType.CONSTANT_WITH_WARMUP: 46 | return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **kwargs) 47 | 48 | # All other schedulers require `num_training_steps` 49 | if num_training_steps is None: 50 | raise ValueError( 51 | f"{name} requires `num_training_steps`, please provide that argument." 52 | ) 53 | 54 | return schedule_func( 55 | optimizer, 56 | num_warmup_steps=num_warmup_steps, 57 | num_training_steps=num_training_steps, 58 | **kwargs, 59 | ) 60 | -------------------------------------------------------------------------------- /BiDP/policies/utils/diffusion/positional_embedding.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | 5 | 6 | class SinusoidalPosEmb(nn.Module): 7 | def __init__(self, dim): 8 | super().__init__() 9 | self.dim = dim 10 | 11 | def forward(self, x): 12 | device = x.device 13 | half_dim = self.dim // 2 14 | emb = math.log(10000) / (half_dim - 1) 15 | emb = torch.exp(torch.arange(half_dim, device=device) * -emb) 16 | emb = x[:, None] * emb[None, :] 17 | emb = torch.cat((emb.sin(), emb.cos()), dim=-1) 18 | return emb 19 | -------------------------------------------------------------------------------- /BiDP/policies/utils/diffusion/resnet_with_gn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torchvision 3 | from torch import nn 4 | 5 | from typing import Callable 6 | 7 | 8 | def get_resnet(name: str, weights=None, **kwargs) -> nn.Module: 9 | """ 10 | name: resnet18, resnet34, resnet50 11 | weights: "IMAGENET1K_V1", None 12 | """ 13 | # Use standard ResNet implementation from torchvision 14 | func = getattr(torchvision.models, name) 15 | resnet = func(weights=weights, **kwargs) 16 | 17 | # remove the final fully connected layer 18 | # for resnet18, the output dim should be 512 19 | resnet.fc = torch.nn.Identity() 20 | return resnet 21 | 22 | 23 | def replace_submodules( 24 | root_module: nn.Module, 25 | predicate: Callable[[nn.Module], bool], 26 | func: Callable[[nn.Module], nn.Module], 27 | ) -> nn.Module: 28 | """ 29 | Replace all submodules selected by the predicate with 30 | the output of func. 31 | 32 | predicate: Return true if the module is to be replaced. 33 | func: Return new module to use. 34 | """ 35 | if predicate(root_module): 36 | return func(root_module) 37 | 38 | bn_list = [ 39 | k.split(".") 40 | for k, m in root_module.named_modules(remove_duplicate=True) 41 | if predicate(m) 42 | ] 43 | for *parent, k in bn_list: 44 | parent_module = root_module 45 | if len(parent) > 0: 46 | parent_module = root_module.get_submodule(".".join(parent)) 47 | if isinstance(parent_module, nn.Sequential): 48 | src_module = parent_module[int(k)] 49 | else: 50 | src_module = getattr(parent_module, k) 51 | tgt_module = func(src_module) 52 | if isinstance(parent_module, nn.Sequential): 53 | parent_module[int(k)] = tgt_module 54 | else: 55 | setattr(parent_module, k, tgt_module) 56 | # verify that all modules are replaced 57 | bn_list = [ 58 | k.split(".") 59 | for k, m in root_module.named_modules(remove_duplicate=True) 60 | if predicate(m) 61 | ] 62 | assert len(bn_list) == 0 63 | return root_module 64 | 65 | 66 | def replace_bn_with_gn( 67 | root_module: nn.Module, features_per_group: int = 16 68 | ) -> nn.Module: 69 | """ 70 | Relace all BatchNorm layers with GroupNorm. 71 | """ 72 | replace_submodules( 73 | root_module=root_module, 74 | predicate=lambda x: isinstance(x, nn.BatchNorm2d), 75 | func=lambda x: nn.GroupNorm( 76 | num_groups=x.num_features // features_per_group, num_channels=x.num_features 77 | ), 78 | ) 79 | return root_module 80 | -------------------------------------------------------------------------------- /BiDP/policies/utils/equivariant_diffusion/conditional_unet1d.py: -------------------------------------------------------------------------------- 1 | from typing import Union 2 | import logging 3 | import torch 4 | import torch.nn as nn 5 | import einops 6 | from einops.layers.torch import Rearrange 7 | 8 | from policies.utils.equivariant_diffusion.conv1d_components import ( 9 | VecDownsample1d, 10 | VecUpsample1d, 11 | VecConv1dBlock, 12 | ) 13 | from policies.utils.diffusion.positional_embedding import SinusoidalPosEmb 14 | from policies.vision.vec_layers import VecLinNormAct as VecLNA 15 | from policies.vision.vec_layers import VecLinear 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | 20 | class VecConditionalResidualBlock1D(nn.Module): 21 | def __init__( 22 | self, 23 | in_channels, 24 | out_channels, 25 | vec_cond_dim, 26 | scalar_cond_dim=0, 27 | kernel_size=3, 28 | cond_predict_scale=False, 29 | ): 30 | # input dimensionality: () 31 | super().__init__() 32 | 33 | self.blocks = nn.ModuleList( 34 | [ 35 | VecConv1dBlock(in_channels, out_channels, kernel_size), 36 | VecConv1dBlock(out_channels, out_channels, kernel_size), 37 | ] 38 | ) 39 | 40 | # FiLM modulation https://arxiv.org/abs/1709.07871 41 | # predicts per-channel scale and bias 42 | cond_channels = out_channels 43 | if cond_predict_scale: 44 | cond_channels = out_channels * 2 45 | self.cond_predict_scale = cond_predict_scale 46 | self.out_channels = out_channels 47 | 48 | act_func = nn.Mish() 49 | vnla_cfg = dict(mode="so3", s_in=scalar_cond_dim, return_tuple=False) 50 | self.cond_encoder_l1 = VecLNA(vec_cond_dim, cond_channels, act_func, **vnla_cfg) 51 | self.cond_encoder_l2 = VecLinear( 52 | cond_channels, out_channels, s_out=out_channels if cond_predict_scale else 0 53 | ) 54 | 55 | # make sure dimensions compatible 56 | self.residual_conv = ( 57 | VecDownsample1d(in_channels, out_channels, 1, 1, 0) 58 | if in_channels != out_channels 59 | else nn.Identity() 60 | ) 61 | 62 | def forward(self, x, vec_cond, scalar_cond=None): 63 | """ 64 | x : [ batch_size x in_channels x 3 x horizon ] 65 | vec_cond : [ batch_size x vec_cond_dim x 3] 66 | scalar_cond : [ batch_size x scalar_cond_dim ] 67 | 68 | returns: 69 | out : [ batch_size x out_channels x 3 x horizon ] 70 | """ 71 | out = self.blocks[0](x) 72 | bias, scale = self.cond_encoder_l2( 73 | self.cond_encoder_l1(vec_cond, s=scalar_cond) 74 | ) 75 | if self.cond_predict_scale: 76 | out = scale[..., None, None] * out + bias[..., None] 77 | else: 78 | out = out + bias[..., None] 79 | out = self.blocks[1](out) 80 | out = out + self.residual_conv(x) 81 | return out 82 | 83 | 84 | class VecConditionalUnet1D(nn.Module): 85 | def __init__( 86 | self, 87 | input_dim, 88 | cond_dim, 89 | scalar_input_dim=0, 90 | scalar_cond_dim=0, 91 | diffusion_step_embed_dim=256, 92 | down_dims=[256, 512, 1024], 93 | kernel_size=3, 94 | cond_predict_scale=False, 95 | ): 96 | super().__init__() 97 | all_dims = [input_dim] + list(down_dims) 98 | start_dim = down_dims[0] 99 | 100 | dsed = diffusion_step_embed_dim 101 | diffusion_step_encoder = nn.Sequential( 102 | SinusoidalPosEmb(dsed), 103 | nn.Linear(dsed, dsed * 4), 104 | nn.Mish(), 105 | nn.Linear(dsed * 4, dsed), 106 | ) 107 | 108 | vec_cond_dim = cond_dim 109 | scalar_cond_dim = dsed + scalar_cond_dim 110 | 111 | if scalar_input_dim > 0: 112 | self.scalar_fusion_module = VecLNA( 113 | input_dim, 114 | down_dims[0], 115 | act_func=nn.Mish(), 116 | s_in=scalar_input_dim, 117 | mode="so3", 118 | return_tuple=False, 119 | ) 120 | all_dims[0] = down_dims[0] 121 | 122 | in_out = list(zip(all_dims[:-1], all_dims[1:])) 123 | 124 | make_res_block = lambda din, dout: VecConditionalResidualBlock1D( 125 | din, 126 | dout, 127 | vec_cond_dim, 128 | scalar_cond_dim=scalar_cond_dim, 129 | kernel_size=kernel_size, 130 | cond_predict_scale=cond_predict_scale, 131 | ) 132 | 133 | mid_dim = all_dims[-1] 134 | self.mid_modules = nn.ModuleList( 135 | [make_res_block(mid_dim, mid_dim), make_res_block(mid_dim, mid_dim)] 136 | ) 137 | 138 | down_modules = nn.ModuleList([]) 139 | for ind, (dim_in, dim_out) in enumerate(in_out): 140 | is_last = ind >= (len(in_out) - 1) 141 | down_modules.append( 142 | nn.ModuleList( 143 | [ 144 | make_res_block(dim_in, dim_out), 145 | make_res_block(dim_out, dim_out), 146 | VecDownsample1d(dim_out) if not is_last else nn.Identity(), 147 | ] 148 | ) 149 | ) 150 | 151 | up_modules = nn.ModuleList([]) 152 | for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])): 153 | is_last = ind >= (len(in_out) - 1) 154 | up_modules.append( 155 | nn.ModuleList( 156 | [ 157 | make_res_block(dim_out * 2, dim_in), 158 | make_res_block(dim_in, dim_in), 159 | VecUpsample1d(dim_in) if not is_last else nn.Identity(), 160 | ] 161 | ) 162 | ) 163 | 164 | final_conv = nn.Sequential( 165 | VecConv1dBlock(start_dim, start_dim, kernel_size=kernel_size), 166 | VecDownsample1d(start_dim, input_dim, 1, 1, 0), 167 | ) 168 | 169 | if scalar_input_dim > 0: 170 | self.final_linear = VecLinear(start_dim, 0, s_out=scalar_input_dim) 171 | 172 | self.diffusion_step_encoder = diffusion_step_encoder 173 | self.up_modules = up_modules 174 | self.down_modules = down_modules 175 | self.final_conv = final_conv 176 | 177 | logger.info( 178 | "number of parameters: %e", sum(p.numel() for p in self.parameters()) 179 | ) 180 | 181 | def forward( 182 | self, 183 | sample: torch.Tensor, 184 | timestep: Union[torch.Tensor, float, int], 185 | scalar_sample=None, 186 | cond=None, 187 | scalar_cond=None, 188 | **kwargs 189 | ): 190 | """ 191 | sample: (B,T,input_dim,3) 192 | timestep: (B,) or int, diffusion step 193 | scalar_sample: (B,T,input_dim) 194 | cond: (B,cond_dim,3) 195 | scalar_cond: (B,cond_dim) 196 | output: (B,T,input_dim) 197 | """ 198 | sample = einops.rearrange(sample, "b h t v -> b t v h") 199 | if scalar_sample is not None: 200 | scalar_sample = einops.rearrange(scalar_sample, "b h t -> b t h") 201 | 202 | # 1. encode timestep 203 | timesteps = timestep 204 | if not torch.is_tensor(timesteps): 205 | # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can 206 | timesteps = torch.tensor( 207 | [timesteps], dtype=torch.long, device=sample.device 208 | ) 209 | elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: 210 | timesteps = timesteps[None].to(sample.device) 211 | # broadcast to batch dimension in a way that's compatible with ONNX/Core ML 212 | timesteps = timesteps.expand(sample.shape[0]) 213 | 214 | # 2. conditioning 215 | assert cond is not None 216 | vec_feature = cond 217 | scalar_feature = self.diffusion_step_encoder(timesteps) # (B, dsed) 218 | if scalar_cond is not None: 219 | scalar_feature = torch.cat([scalar_feature, scalar_cond], dim=-1) 220 | 221 | # 3. forward pass through the unet 222 | # 3.1 fuse vector and scalar samples if needed 223 | if scalar_sample is not None: 224 | assert hasattr(self, "scalar_fusion_module") 225 | sample = self.scalar_fusion_module(sample, scalar_sample) 226 | 227 | # 3.2 unet 228 | x = sample 229 | h = [] 230 | for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules): 231 | x = resnet(x, vec_feature, scalar_feature) 232 | x = resnet2(x, vec_feature, scalar_feature) 233 | h.append(x) 234 | x = downsample(x) 235 | 236 | for mid_module in self.mid_modules: 237 | x = mid_module(x, vec_feature, scalar_feature) 238 | 239 | for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules): 240 | x = torch.cat((x, h.pop()), dim=1) 241 | x = resnet(x, vec_feature, scalar_feature) 242 | x = resnet2(x, vec_feature, scalar_feature) 243 | x = upsample(x) 244 | 245 | if scalar_sample is not None: 246 | assert hasattr(self, "final_linear") 247 | x_scalar = self.final_linear(x)[1] 248 | x_scalar = einops.rearrange(x_scalar, "b t h -> b h t") 249 | x = self.final_conv(x) 250 | x = einops.rearrange(x, "b t v h -> b h t v") 251 | 252 | if scalar_sample is not None: 253 | return x, x_scalar 254 | else: 255 | return x, None 256 | -------------------------------------------------------------------------------- /BiDP/policies/utils/equivariant_diffusion/conv1d_components.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | from policies.vision.vec_layers import VecActivation 6 | 7 | 8 | class VecDownsample1d(nn.Module): 9 | def __init__(self, in_dim, out_dim=None, kernel_size=3, stride=2, padding=1): 10 | # input: (N, C_in, 3, L) 11 | super().__init__() 12 | if out_dim is None: 13 | out_dim = in_dim 14 | 15 | # note: we need to omit the bias term to maintain equivariance 16 | self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, stride, padding, bias=False) 17 | 18 | def forward(self, x): 19 | # put vector dimension inside batch dimension 20 | original_shape = x.shape 21 | x_flattened = torch.transpose(x, 1, 2) 22 | x_flattened = x_flattened.reshape(-1, *x_flattened.shape[2:]) 23 | # pass processed input through convolution layer 24 | out = self.conv(x_flattened) 25 | # process convolved input back to vector format 26 | out = torch.transpose(out.reshape(-1, 3, *out.shape[1:]), 1, 2) 27 | return out 28 | 29 | 30 | class VecUpsample1d(VecDownsample1d): 31 | def __init__(self, dim): 32 | nn.Module.__init__(self) 33 | self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1, bias=False) 34 | 35 | 36 | class VecConv1dBlock(nn.Module): 37 | """ 38 | Conv1d --> GroupNorm --> Mish 39 | """ 40 | 41 | def __init__(self, inp_channels, out_channels, kernel_size): 42 | # input dimension: (N, C_in, 3, L) 43 | super().__init__() 44 | 45 | # Note: we cannot have normalization layers here because it breaks scale equivariance 46 | self.block = nn.Sequential( 47 | VecDownsample1d( 48 | inp_channels, out_channels, kernel_size, 1, kernel_size // 2 49 | ), 50 | VecActivation( 51 | out_channels, 52 | nn.Mish(), 53 | shared_nonlinearity=False, 54 | mode="so3", 55 | cross=False, 56 | ), 57 | ) 58 | 59 | def forward(self, x): 60 | return self.block(x) 61 | 62 | 63 | def test(): 64 | cb = VecConv1dBlock(256, 128, kernel_size=3) 65 | x = torch.zeros((1, 256, 3, 16)) 66 | o = cb(x) 67 | -------------------------------------------------------------------------------- /BiDP/policies/utils/media.py: -------------------------------------------------------------------------------- 1 | """ 2 | Utilities for loading, saving, and manipulating videos and images. 3 | 4 | @yjy0625 5 | 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | import cv2 11 | import skvideo.io 12 | import imageio 13 | 14 | 15 | def _make_dir(filename): 16 | folder = os.path.dirname(filename) 17 | os.makedirs(folder, exist_ok=True) 18 | 19 | 20 | def save_image(image, filename): 21 | _make_dir(filename) 22 | if np.max(image) < 2: 23 | image = np.array(image * 255) 24 | image = image.astype(np.uint8) 25 | cv2.imwrite(filename, image[..., ::-1]) 26 | 27 | 28 | def save_gif(video_array, file_path, fps=10): 29 | """ 30 | Save a (T, H, W, 3) numpy array of video into a GIF file. 31 | 32 | Parameters: 33 | video_array (numpy.ndarray): The video as a 4D numpy array with shape (T, H, W, 3). 34 | file_path (str): The file path where the GIF will be saved. 35 | fps (int, optional): Frames per second for the GIF. Default is 10. 36 | """ 37 | try: 38 | # Ensure the video array is uint8 (required for GIF) 39 | video_array = (255 * (1.0 - video_array)).astype("uint8") 40 | 41 | # Save the GIF 42 | imageio.mimsave(file_path, video_array, duration=len(video_array) / fps, loop=1) 43 | print(f"Saved GIF to {file_path}") 44 | except Exception as e: 45 | print(f"Error saving GIF: {e}") 46 | 47 | 48 | def save_video(video_frames, filename, fps=10, video_format="mp4"): 49 | if len(video_frames) == 0: 50 | return False 51 | 52 | assert fps == int(fps), fps 53 | _make_dir(filename) 54 | 55 | skvideo.io.vwrite( 56 | filename, 57 | video_frames, 58 | inputdict={ 59 | "-r": str(int(fps)), 60 | }, 61 | outputdict={"-f": video_format, "-pix_fmt": "yuv420p"}, 62 | ) 63 | 64 | return True 65 | 66 | 67 | def read_video(filename): 68 | return skvideo.io.vread(filename) 69 | 70 | 71 | def get_video_framerate(filename): 72 | videometadata = skvideo.io.ffprobe(filename) 73 | frame_rate = videometadata["video"]["@avg_frame_rate"] 74 | return eval(frame_rate) 75 | 76 | 77 | def add_caption_to_img(img, info, name=None, flip_rgb=False, num_lines=5): 78 | """Adds caption to an image. info is dict with keys and text/array. 79 | :arg name: if given this will be printed as heading in the first line 80 | :arg flip_rgb: set to True for inputs with BGR color channels 81 | """ 82 | mul = 2.0 83 | offset = int(12 * mul) 84 | 85 | frame = img * 255.0 if img.max() <= 1.0 else img 86 | if flip_rgb: 87 | frame = frame[:, :, ::-1] 88 | 89 | # colors 90 | blue = (66, 133, 244) 91 | yellow = (255, 255, 0) 92 | white = (255, 255, 255) 93 | 94 | # add border to frame if success 95 | if "is_success" in info.keys() and info["is_success"]: 96 | border_size = int(10 * mul) 97 | frame[:border_size, :] = np.array(blue)[None][None] 98 | frame[-border_size:, :] = np.array(blue)[None][None] 99 | frame[:, :border_size] = np.array(blue)[None][None] 100 | frame[:, -border_size:] = np.array(blue)[None][None] 101 | 102 | fheight, fwidth = frame.shape[:2] 103 | pad_height = int(offset * (num_lines + 2)) 104 | frame = np.concatenate([frame, np.zeros((pad_height, fwidth, 3))], 0) 105 | 106 | font_size = 0.4 * mul 107 | thickness = int(1 * mul) 108 | x, y = int(5 * mul), fheight + int(10 * mul) 109 | if name is not None: 110 | cv2.putText( 111 | frame, 112 | "[{}]".format(name), 113 | (x, y), 114 | cv2.FONT_HERSHEY_SIMPLEX, 115 | font_size, 116 | yellow, 117 | thickness, 118 | cv2.LINE_AA, 119 | ) 120 | for i, k in enumerate(info.keys()): 121 | v = info[k] 122 | if ( 123 | type(v) == np.ndarray 124 | or type(v) == np.float64 125 | or type(v) == np.float32 126 | or type(v) == float 127 | ): 128 | if type(v) == np.ndarray: 129 | v = np.round(v, 3 if np.isscalar(v) or len(v) <= 3 else 2) 130 | else: 131 | v = np.round(v, 3) 132 | key_text = "{}: ".format(k) 133 | (key_width, _), _ = cv2.getTextSize( 134 | key_text, cv2.FONT_HERSHEY_SIMPLEX, font_size, thickness 135 | ) 136 | 137 | cv2.putText( 138 | frame, 139 | key_text, 140 | (x, y + offset * (i + 2)), 141 | cv2.FONT_HERSHEY_SIMPLEX, 142 | font_size, 143 | blue, 144 | thickness, 145 | cv2.LINE_AA, 146 | ) 147 | 148 | cv2.putText( 149 | frame, 150 | str(v), 151 | (x + key_width, y + offset * (i + 2)), 152 | cv2.FONT_HERSHEY_SIMPLEX, 153 | font_size, 154 | white, 155 | thickness, 156 | cv2.LINE_AA, 157 | ) 158 | 159 | if flip_rgb: 160 | frame = frame[:, :, ::-1] 161 | 162 | return frame 163 | 164 | 165 | def add_border_to_img(frame, color=(234, 67, 53)): 166 | border_size = 20 167 | if np.max(frame) <= 1.0: 168 | color = tuple([float(x) / 255 for x in color]) 169 | frame[:border_size, :] = np.array(color)[None][None] 170 | frame[-border_size:, :] = np.array(color)[None][None] 171 | frame[:, :border_size] = np.array(color)[None][None] 172 | frame[:, -border_size:] = np.array(color)[None][None] 173 | return frame 174 | 175 | 176 | def add_border_to_video(frames, color=(234, 67, 53)): 177 | border_size = 20 178 | if np.max(frames) <= 1.0: 179 | color = tuple([float(x) / 255 for x in color]) 180 | frames[:, :border_size, :] = np.array(color)[None][None][None] 181 | frames[:, -border_size:, :] = np.array(color)[None][None][None] 182 | frames[:, :, :border_size] = np.array(color)[None][None][None] 183 | frames[:, :, -border_size:] = np.array(color)[None][None][None] 184 | return frames 185 | 186 | 187 | def combine_videos(images, num_cols=5): 188 | if len(images) == 1: 189 | return np.array(images[0]) 190 | max_frames = np.max([len(im) for im in images]) 191 | images = [ 192 | np.concatenate([im[:-1], np.array([im[-1]] * (max_frames - len(im) + 1))]) 193 | for im in images 194 | ] 195 | images = np.array(images) 196 | B = images.shape[0] 197 | if B % num_cols != 0: 198 | images = np.concatenate( 199 | [images, np.zeros((num_cols - (B % num_cols),) + tuple(images.shape[1:]))] 200 | ) 201 | B, T, H, W, C = images.shape 202 | images = images.reshape(B // num_cols, num_cols, T, H, W, C).transpose( 203 | 2, 0, 3, 1, 4, 5 204 | ) 205 | images = images.reshape(T, B // num_cols * H, num_cols * W, C) 206 | return images 207 | -------------------------------------------------------------------------------- /BiDP/policies/utils/misc.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | 5 | def to_torch(batch, device): 6 | return {k: v.to(device) for k, v in batch.items()} 7 | 8 | 9 | def rotate_around_z( 10 | points, 11 | angle_rad=0.0, 12 | center=np.array([0.0, 0.0, 0.0]), 13 | scale=np.array([1.0, 1.0, 1.0]), 14 | ): 15 | # Check if the input points have the correct shape (N, 3) 16 | assert (len(points.shape) == 1 and len(points) == 3) or points.shape[-1] == 3 17 | p_shape = points.shape 18 | points = points.reshape(-1, 3) - center[None] 19 | 20 | # Create the rotation matrix 21 | cos_theta = np.cos(angle_rad) 22 | sin_theta = np.sin(angle_rad) 23 | rotation_matrix = np.array( 24 | [[cos_theta, -sin_theta, 0], [sin_theta, cos_theta, 0], [0, 0, 1]] 25 | ) 26 | 27 | # Apply the rotation to all points using matrix multiplication 28 | rotated_points = np.dot(points, rotation_matrix.T) * scale[None] + center[None] 29 | rotated_points = rotated_points.reshape(p_shape) 30 | 31 | return rotated_points 32 | 33 | 34 | def get_env_class(env_name): 35 | if env_name == "fold": 36 | from envs.sim_mobile.folding_env import FoldingEnv 37 | return FoldingEnv 38 | elif env_name == "cover": 39 | from envs.sim_mobile.covering_env import CoveringEnv 40 | return CoveringEnv 41 | elif env_name == "close": 42 | from envs.sim_mobile.closing_env import ClosingEnv 43 | return ClosingEnv 44 | else: 45 | raise ValueError() 46 | 47 | 48 | def get_dataset(cfg, mode="train"): 49 | # from policies.datasets.dataset import BaseDataset 50 | from policies.datasets.dataset_loader import BaseDataset 51 | return BaseDataset(cfg.data.dataset, mode) 52 | 53 | 54 | def get_agent(agent_name): 55 | if agent_name == "dp": 56 | from policies.agents.dp_agent import DPAgent 57 | return DPAgent 58 | elif agent_name == "equibot": 59 | from policies.agents.equibot_agent import EquiBotAgent 60 | return EquiBotAgent 61 | else: 62 | raise ValueError(f"Agent with name [{agent_name}] not found.") 63 | -------------------------------------------------------------------------------- /BiDP/policies/utils/norm.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | 5 | class Normalizer(object): 6 | def __init__(self, data, symmetric=False, indices=None): 7 | if isinstance(data, dict): 8 | # load from existing data statistics 9 | self.stats = data 10 | elif symmetric: 11 | # just scaling applied in normalization, no bias 12 | # perform the same normalization in groups 13 | if indices is None: 14 | indices = np.arange(data.shape[-1])[None] 15 | 16 | self.stats = { 17 | "min": torch.zeros([data.shape[-1]]).to(data.device), 18 | "max": torch.ones([data.shape[-1]]).to(data.device), 19 | } 20 | for group in indices: 21 | max_abs = torch.abs(data[:, group]).max(0)[0].detach() 22 | limits = torch.ones_like(max_abs) * torch.max(max_abs) 23 | self.stats["max"][group] = limits 24 | else: 25 | mask = torch.zeros([data.shape[-1]]).to(data.device) 26 | if indices is not None: 27 | mask[indices.flatten()] += 1 28 | else: 29 | mask += 1 30 | self.stats = { 31 | "min": data.min(0)[0].detach() * mask, 32 | "max": data.max(0)[0].detach() * mask + 1.0 * (1 - mask), 33 | } 34 | 35 | def normalize(self, data): 36 | nd = len(data.shape) 37 | target_shape = (1,) * (nd - 1) + (data.shape[-1],) 38 | dmin = self.stats["min"].reshape(target_shape) 39 | dmax = self.stats["max"].reshape(target_shape) 40 | return (data - dmin) / (dmax - dmin + 1e-12) 41 | 42 | def unnormalize(self, data): 43 | nd = len(data.shape) 44 | target_shape = (1,) * (nd - 1) + (data.shape[-1],) 45 | dmin = self.stats["min"].reshape(target_shape) 46 | dmax = self.stats["max"].reshape(target_shape) 47 | return data * (dmax - dmin) + dmin 48 | 49 | def state_dict(self): 50 | return self.stats 51 | 52 | def load_state_dict(self, state_dict): 53 | self.stats = state_dict 54 | -------------------------------------------------------------------------------- /BiDP/policies/vision/misc.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | 4 | def cfg_with_default(cfg, key_list, default): 5 | root = cfg 6 | for k in key_list: 7 | if k in root.keys(): 8 | root = root[k] 9 | else: 10 | return default 11 | return root 12 | 13 | 14 | def count_param(network_dict): 15 | for k in network_dict: 16 | logging.info( 17 | "{:.3f}M params in {}".format( 18 | sum(param.numel() for param in network_dict[k].parameters()) / 1e6, k 19 | ) 20 | ) 21 | -------------------------------------------------------------------------------- /BiDP/policies/vision/pointnet_encoder.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import numpy as np 4 | import torch 5 | import torch.nn as nn 6 | import torch.nn.functional as F 7 | from pytorch3d.ops.knn import knn_points 8 | import logging 9 | 10 | 11 | def meanpool(x, dim=-1, keepdim=False): 12 | out = x.mean(dim=dim, keepdim=keepdim) 13 | return out 14 | 15 | 16 | class PointNetEncoder(nn.Module): 17 | def __init__(self, h_dim=128, c_dim=128, num_layers=4, **kwargs): 18 | super().__init__() 19 | 20 | self.h_dim = h_dim 21 | self.c_dim = c_dim 22 | self.num_layers = num_layers 23 | 24 | self.pool = meanpool 25 | 26 | act_func = nn.LeakyReLU(negative_slope=0.0, inplace=False) 27 | self.act = act_func 28 | 29 | self.conv_in = nn.Conv1d(3, h_dim, kernel_size=1) 30 | self.layers, self.global_layers = nn.ModuleList(), nn.ModuleList() 31 | for i in range(self.num_layers): 32 | self.layers.append(nn.Conv1d(h_dim, h_dim, kernel_size=1)) 33 | self.global_layers.append(nn.Conv1d(h_dim * 2, h_dim, kernel_size=1)) 34 | self.conv_out = nn.Conv1d(h_dim * self.num_layers, c_dim, kernel_size=1) 35 | 36 | def forward(self, x, ret_perpoint_feat=False): 37 | 38 | y = self.act(self.conv_in(x)) 39 | feat_list = [] 40 | for i in range(self.num_layers): 41 | y = self.act(self.layers[i](y)) 42 | y_global = y.max(-1, keepdim=True).values 43 | y = torch.cat([y, y_global.expand_as(y)], dim=1) 44 | y = self.act(self.global_layers[i](y)) 45 | feat_list.append(y) 46 | x = torch.cat(feat_list, dim=1) 47 | x = self.conv_out(x) 48 | 49 | x_global = x.max(-1).values 50 | 51 | ret = {"global": x_global} 52 | if ret_perpoint_feat: 53 | ret["per_point"] = x.transpose(1, 2) 54 | 55 | return ret 56 | -------------------------------------------------------------------------------- /BiDP/policies/vision/sim3_encoder.py: -------------------------------------------------------------------------------- 1 | # using the backbone, different encoders 2 | 3 | import os 4 | import sys 5 | import numpy as np 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | import logging 10 | 11 | from policies.vision.vec_layers import VecLinear 12 | from policies.vision.vec_pointnet import VecPointNet 13 | 14 | 15 | BACKBONE_DICT = {"vn_pointnet": VecPointNet} 16 | 17 | NORMALIZATION_METHOD = {"bn": nn.BatchNorm1d, "in": nn.InstanceNorm1d} 18 | 19 | 20 | class SIM3Vec4Latent(nn.Module): 21 | """ 22 | This encoder encode the input point cloud to 4 latents 23 | Now only support so3 mode 24 | TODO: se3 and hybrid 25 | """ 26 | 27 | def __init__( 28 | self, 29 | c_dim, 30 | backbone_type, 31 | backbone_args, 32 | mode="so3", 33 | normalization_method=None, 34 | ): 35 | super().__init__() 36 | assert mode == "so3", NotImplementedError("TODO, add se3") 37 | if normalization_method is not None: 38 | backbone_args["normalization_method"] = NORMALIZATION_METHOD[ 39 | normalization_method 40 | ] 41 | 42 | self.backbone = BACKBONE_DICT[backbone_type](**backbone_args) 43 | self.fc_inv = VecLinear(c_dim, c_dim, mode=mode) 44 | 45 | def forward(self, pcl, ret_perpoint_feat=False, target_norm=1.0): 46 | B, T, N, _ = pcl.shape 47 | pcl = pcl.view(B * T, -1, 3).transpose(1, 2) # [BT, 3, N] 48 | 49 | centroid = pcl.mean(-1, keepdim=True) # B,3,1 50 | input_pcl = pcl - centroid 51 | 52 | z_scale = input_pcl.norm(dim=1).mean(-1) / target_norm # B 53 | z_center = centroid.permute(0, 2, 1) 54 | 55 | input_pcl = input_pcl / z_scale[:, None, None] 56 | 57 | x, x_perpoint = self.backbone(input_pcl) # B,C,3 58 | 59 | z_so3 = x 60 | z_inv_dual, _ = self.fc_inv(x[..., None]) 61 | z_inv_dual = z_inv_dual.squeeze(-1) 62 | z_inv = (z_inv_dual * z_so3).sum(-1) 63 | 64 | ret = { 65 | "inv": z_inv, 66 | "so3": z_so3, 67 | "scale": z_scale, 68 | "center": z_center, 69 | } 70 | 71 | if ret_perpoint_feat: 72 | ret["per_point_so3"] = x_perpoint # [B, C, 3, N] 73 | 74 | return ret 75 | -------------------------------------------------------------------------------- /BiDP/policies/vision/vec_pointnet.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import numpy as np 4 | import torch 5 | import torch.nn as nn 6 | import torch.nn.functional as F 7 | from pytorch3d.ops.knn import knn_points 8 | import logging 9 | 10 | from policies.vision.vec_layers import VecLinear 11 | from policies.vision.vec_layers import VecLinNormAct as VecLNA 12 | 13 | 14 | def meanpool(x, dim=-1, keepdim=False): 15 | out = x.mean(dim=dim, keepdim=keepdim) 16 | return out 17 | 18 | 19 | class VecPointNet(nn.Module): 20 | def __init__( 21 | self, 22 | h_dim=128, 23 | c_dim=128, 24 | num_layers=4, 25 | knn=16, 26 | ): 27 | super().__init__() 28 | 29 | self.h_dim = h_dim 30 | self.c_dim = c_dim 31 | self.num_layers = num_layers 32 | self.knn = knn 33 | 34 | self.pool = meanpool 35 | 36 | act_func = nn.LeakyReLU(negative_slope=0.0, inplace=False) 37 | vnla_cfg = {"mode": "so3", "act_func": act_func} 38 | 39 | self.conv_in = VecLNA(3, h_dim, **vnla_cfg) 40 | self.layers, self.global_layers = nn.ModuleList(), nn.ModuleList() 41 | for i in range(self.num_layers): 42 | self.layers.append(VecLNA(h_dim, h_dim, **vnla_cfg)) 43 | self.global_layers.append(VecLNA(h_dim * 2, h_dim, **vnla_cfg)) 44 | self.conv_out = VecLinear(h_dim * self.num_layers, c_dim, mode="so3") 45 | 46 | self.fc_inv = VecLinear(c_dim, 3, mode="so3") 47 | 48 | def get_graph_feature(self, x: torch.Tensor, k: int, knn_idx=None, cross=False): 49 | # x: B,C,3,N return B,C*2,3,N,K 50 | 51 | 52 | B, C, _, N = x.shape 53 | if knn_idx is None: 54 | # if knn_idx is not none, compute the knn by x distance; ndf use fixed knn as input topo 55 | _x = x.reshape(B, -1, N) 56 | _, knn_idx, neighbors = knn_points( 57 | _x.transpose(2, 1), _x.transpose(2, 1), K=k, return_nn=True 58 | ) # B,N,K; B,N,K; B,N,K,D 59 | neighbors = neighbors.reshape(B, N, k, C, 3).permute(0, -2, -1, 1, 2) 60 | else: # gather from the input knn idx 61 | assert knn_idx.shape[-1] == k, f"input knn gather idx should have k={k}" 62 | neighbors = torch.gather( 63 | x[..., None, :].expand(-1, -1, -1, N, -1), 64 | dim=-1, 65 | index=knn_idx[:, None, None, ...].expand(-1, C, 3, -1, -1), 66 | ) # B,C,3,N,K 67 | x_padded = x[..., None].expand_as(neighbors) 68 | 69 | if cross: 70 | x_dir = F.normalize(x, dim=2) 71 | x_dir_padded = x_dir[..., None].expand_as(neighbors) 72 | cross = torch.cross(x_dir_padded, neighbors, dim=2) 73 | y = torch.cat([cross, neighbors - x_padded, x_padded], 1) 74 | else: 75 | y = torch.cat([neighbors - x_padded, x_padded], 1) 76 | return y, knn_idx # B,C*2,3,N,K 77 | 78 | def forward(self, x): 79 | x = x.unsqueeze(1) # [B, 1, 3, N] 80 | 81 | x, knn_idx = self.get_graph_feature(x, self.knn, cross=True) 82 | x, _ = self.conv_in(x) 83 | x = self.pool(x) 84 | 85 | y = x 86 | feat_list = [] 87 | for i in range(self.num_layers): 88 | y, _ = self.layers[i](y) 89 | y_global = y.mean(-1, keepdim=True) 90 | y = torch.cat([y, y_global.expand_as(y)], dim=1) 91 | y, _ = self.global_layers[i](y) 92 | feat_list.append(y) 93 | x = torch.cat(feat_list, dim=1) 94 | x, _ = self.conv_out(x) 95 | 96 | return x.mean(-1), x 97 | -------------------------------------------------------------------------------- /BiDP/run_noaug.sh: -------------------------------------------------------------------------------- 1 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_drawer_noaug/ckpt01999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=drawer model.pred_horizon=12 data.dataset.is_transformed=0 2 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_pouring_noaug/ckpt01999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=pouring model.pred_horizon=12 data.dataset.is_transformed=0 3 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_unscrew_noaug/ckpt03999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=unscrew model.pred_horizon=12 data.dataset.is_transformed=0 4 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_uncover_noaug/ckpt03999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=uncover model.pred_horizon=24 data.dataset.is_transformed=0 5 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_openbox_noaug/ckpt03999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=openbox model.pred_horizon=32 data.dataset.is_transformed=0 -------------------------------------------------------------------------------- /BiDP/run_withaug.sh: -------------------------------------------------------------------------------- 1 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_drawer_withaug/ckpt00499.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=drawer model.pred_horizon=12 data.dataset.is_transformed=0 2 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_pouring_withaug/ckpt00499.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=pouring model.pred_horizon=12 data.dataset.is_transformed=0 3 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_unscrew_withaug/ckpt00999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=unscrew model.pred_horizon=12 data.dataset.is_transformed=0 4 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_uncover_withaug/ckpt00999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=uncover model.pred_horizon=24 data.dataset.is_transformed=0 5 | python infer.py --config-name basic training.ckpt=/home/dexforce/zhouhuayi/projects/BiDP/logs/train/bidp_openbox_withaug/ckpt00999.pth data.dataset.path=/home/dexforce/zhouhuayi/projects/BiDP/dataset agent.agent_name=equibot env.env_class=openbox model.pred_horizon=32 data.dataset.is_transformed=0 -------------------------------------------------------------------------------- /BiDP/test/drawer/demo_notrans_noaug_id/BiDP_infer_demo1.mp4: -------------------------------------------------------------------------------- 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tqdm import tqdm 11 | from glob import glob 12 | from omegaconf import OmegaConf 13 | 14 | from policies.utils.media import save_video 15 | from policies.utils.misc import get_dataset, get_agent 16 | 17 | 18 | @hydra.main(config_path="policies/configs", config_name="basic") 19 | def main(cfg): 20 | assert cfg.mode == "train" 21 | np.random.seed(cfg.seed) 22 | 23 | # initialize parameters 24 | batch_size = cfg.training.batch_size 25 | 26 | # setup logging 27 | if cfg.use_wandb: 28 | wandb_config = omegaconf.OmegaConf.to_container( 29 | cfg, resolve=True, throw_on_missing=False 30 | ) 31 | wandb.init( 32 | entity=cfg.wandb.entity, 33 | project=cfg.wandb.project, 34 | tags=["train"], 35 | name=cfg.prefix, 36 | settings=wandb.Settings(code_dir="."), 37 | config=wandb_config, 38 | ) 39 | log_dir = os.getcwd() 40 | 41 | # init dataloader 42 | train_dataset = get_dataset(cfg, "train") 43 | num_workers = cfg.data.dataset.num_workers 44 | train_loader = torch.utils.data.DataLoader( 45 | train_dataset, 46 | batch_size=batch_size, 47 | num_workers=num_workers, 48 | shuffle=True, 49 | drop_last=True, 50 | pin_memory=True, 51 | ) 52 | cfg.data.dataset.num_training_steps = ( 53 | cfg.training.num_epochs * len(train_dataset) // batch_size 54 | ) 55 | 56 | # init agent 57 | agent = get_agent(cfg.agent.agent_name)(cfg) 58 | if cfg.training.ckpt is not None: 59 | agent.load_snapshot(cfg.training.ckpt) 60 | start_epoch_ix = int(cfg.training.ckpt.split("/")[-1].split(".")[0][4:]) 61 | else: 62 | start_epoch_ix = 0 63 | 64 | # train loop 65 | global_step = 0 66 | for epoch_ix in tqdm(range(start_epoch_ix, cfg.training.num_epochs)): 67 | batch_ix = 0 68 | for batch in tqdm(train_loader, leave=False, desc="Batches"): 69 | train_metrics = agent.update( 70 | batch, vis=epoch_ix % cfg.training.vis_interval == 0 and batch_ix == 0 71 | ) 72 | if cfg.use_wandb: 73 | wandb.log( 74 | {"train/" + k: v for k, v in train_metrics.items()}, 75 | step=global_step, 76 | ) 77 | wandb.log({"epoch": epoch_ix}, step=global_step) 78 | del train_metrics 79 | global_step += 1 80 | batch_ix += 1 81 | 82 | if ( 83 | epoch_ix % cfg.training.save_interval == 0 84 | or epoch_ix == cfg.training.num_epochs - 1 85 | ): 86 | save_path = os.path.join(log_dir, f"ckpt{epoch_ix:05d}.pth") 87 | num_ckpt_to_keep = 2 # 10 88 | if len(list(glob(os.path.join(log_dir, "ckpt*.pth")))) > num_ckpt_to_keep: 89 | # remove old checkpoints 90 | for fn in list(sorted(glob(os.path.join(log_dir, "ckpt*.pth"))))[ 91 | :-num_ckpt_to_keep 92 | ]: 93 | os.remove(fn) 94 | agent.save_snapshot(save_path) 95 | 96 | 97 | if __name__ == "__main__": 98 | main() 99 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # YOTO 2 | Code for my paper "*You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations*" [[arXiv](https://arxiv.org/abs/2501.14208)] / [[Project](https://hnuzhy.github.io/projects/YOTO/)] / [[Dataset](https://huggingface.co/HoyerChou/YOTO)] 3 | 4 | - :white_check_mark: [**2025-04-11**] Our paper has been accepted by conference **RSS 2025** ([Robotics: Science and Systems](https://roboticsconference.org/)). 5 | - :white_check_mark: [**2025-03-10**] We have uploaded our preprocessed datasets and pretrained models in [huggingface/YOTO](https://huggingface.co/HoyerChou/YOTO). Please refer [AugDemos](https://github.com/hnuzhy/YOTO/tree/main/AugDemos) and [BiDP](https://github.com/hnuzhy/YOTO/tree/main/BiDP) under this repo for the usage of them. 6 | 7 | *** 8 | 9 | ## ● Abstract 10 | 11 | > Bimanual robotic manipulation is a long-standing challenge of embodied intelligence due to its characteristics of dual-arm spatial-temporal coordination and high-dimensional action spaces. Previous studies rely on pre-defined action taxonomies or direct teleoperation to alleviate or circumvent these issues, often making them lack simplicity, versatility and scalability. Differently, we believe that the most effective and efficient way for teaching bimanual manipulation is learning from human demonstrated videos, where rich features such as spatial-temporal positions, dynamic postures, interaction states and dexterous transitions are available almost for free. In this work, we propose the YOTO (You Only Teach Once), which can extract and then inject patterns of bimanual actions from as few as a single binocular observation of hand movements, and teach dual robot arms various complex tasks. Furthermore, based on keyframes-based motion trajectories, we devise a subtle solution for rapidly generating training demonstrations with diverse variations of manipulated objects and their locations. These data can then be used to learn a customized bimanual diffusion policy (BiDP) across diverse scenes. In experiments, YOTO achieves impressive performance in mimicking 5 intricate long-horizon bimanual tasks, possesses strong generalization under different visual and spatial conditions, and outperforms existing visuomotor imitation learning methods in accuracy and efficiency. 12 | 13 | ## ● Inference Visualization 14 | 15 | * Below, we present some prediction results of trained models with / without augmentation for comparison. 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 |
Task BiDP trained without augmentation BiDP trained with augmentation
Drawer
Pouring
Unscrew
Uncover
Openbox
49 | 50 | * *It can be clearly seen that the augmented data can make the model prediction error significantly smaller.* More videos and illustrations can be found in our [homepage](https://hnuzhy.github.io/projects/YOTO/). 51 | * Specifically, we use $$\color{green}green$$ point clouds to represent the platform (which does not appear in the observation input) for easy visualization, and $$\color{magenta}magenta$$ to represent the point clouds of the manipulated objects. The $$\color{blue}blue$$ and $$\color{red}red$$ colored 6-DoF keyposes represent the end-effector actions of the left and right arms, respectively. The far left and far right keyposes are initial robot states. The ground-truth 6-DoF keyposes have larger size than those predicted actions. 52 | 53 | 54 | ## ● Acknowledgement 55 | Our `hand motion extraction and injection` process relies on a variety of vison algorithms, including Hand Detection and 3D Mesh Reconstruction [WiLoR](https://github.com/rolpotamias/WiLoR), Large Vision-Language Model [Florence2](https://huggingface.co/collections/microsoft/florence-6669f44df0d87d9c3bfb76de), Segment Anything Model 2 [SAM2](https://github.com/facebookresearch/segment-anything-2) and Binocular Stereo Matching [IGEV](https://github.com/gangweiX/IGEV). While, the codebase of our imitation learning algorithm `BiDP` is partly based on [ACT](https://github.com/tonyzhaozh/act), [Diffusion Policy](https://github.com/real-stanford/diffusion_policy), [3D Diffusion Policy](https://github.com/YanjieZe/3D-Diffusion-Policy) and [EquiBot](https://github.com/yjy0625/equibot). We thank them for their open source efforts and contributions. 56 | 57 | ## ● Citation 58 | If you use our code or models in your research, please cite with: 59 | ```bash 60 | @article{zhou2025you, 61 | title={You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations}, 62 | author={Zhou, Huayi and Wang, Ruixiang and Tai, Yunxin and Deng, Yueci and Liu, Guiliang and Jia, Kui}, 63 | journal={arXiv preprint arXiv:2501.14208}, 64 | year={2025} 65 | } 66 | ``` 67 | --------------------------------------------------------------------------------