├── .gitattributes ├── .github └── FUNDING.yml ├── .gitignore ├── Causal └── PR Causal Introduction.md ├── Control ├── 1_LQR.md ├── 1_LQR_code.py ├── 2_DMP.md └── 3_DMP2.md ├── Efficient ├── PR Efficient Ⅰ：机器人中的数据高效强化学习.md ├── PR Efficient Ⅱ：Bayesian Transfer RL with prior knowledge.md ├── PR Efficient Ⅲ：Efficient RL for Multi-Step Visual Tasks with Sim2real.md ├── PR Efficient Ⅳ：五分钟内让四足机器人学会行走.md ├── PR Efficient Ⅴ：DERL with self-predictive representations.md ├── PR Efficient Ⅵ：从RL的五个方面分析Sample Efficient.md └── PR Efficient Ⅶ：Efficient RL 中表征学习的理论基础.md ├── Federated Learning └── FLⅠ：联邦学习(Federated Learning)入门指南.md ├── Imitation learning ├── 01Introduction.md ├── 02DAgger.assets │ ├── image-20200514200538762.png │ ├── 微信截图_20200514220002.png │ ├── 微信截图_20200514220137.png │ ├── 微信截图_20200514220221.png │ ├── 微信截图_20200514220412.png │ ├── 微信截图_20200514220508.png │ └── 微信截图_20200514220651.png ├── 02DAgger.md ├── 03EnsembleDAgger.assets │ ├── image-20200514223244922.png │ └── 微信截图_20200514225330.png ├── 03EnsembleDAgger.md ├── EnsembleDAgger.assets │ ├── image-20200514131357018.png │ └── image-20200514131458469.png └── Introduction.assets │ ├── 1_9gdENk_iThuoha-ZJK4oOQ.jpeg │ ├── 1_P076bt-xcC3mKyYCINzSFg.jpeg │ ├── 1_RkCKUyRW68fAuysDgWhuMA.png │ ├── 1_UuY1bsit07pwijg1pSOQgQ.jpeg │ ├── image-20200513095032814.png │ ├── image-20200513095203592.png │ ├── image-20200513110523699.png │ ├── stacking_demo.gif │ ├── v2-61bb833f9464f5c7fc088045f26c909d_1440w.png │ └── v2-9ad04f29683121bb7870e0589b8ec389_1440w.png ├── LICENSE ├── MARL ├── MARL Ⅰ：A Selective Overview of Theories and Algorithms.assets │ ├── 2020-09-06_23-17-55.jpg │ ├── 2020-09-08_12-50-46.jpg │ ├── image-20200906172748898.png │ └── image-20200906225644703.png ├── MARL Ⅰ：A Selective Overview of Theories and Algorithms.md ├── MARL Ⅱ：QD-learning.assets │ └── image-20200914210232950.png └── MARL Ⅱ：QD-learning.md ├── MBRL ├── Model-Based RL Ⅲ 从源码读懂PILCO.md └── img │ ├── equation.svg │ ├── image-20200505172434162.png │ ├── 微信截图_20200505223246.png │ └── 微信截图_20200505223309.png ├── Memory └── Memory systems 2018 – towards a new paradigm.md ├── Paper Reading ├── Bayesian Relational Memory for Semantic Visual Navigation.assets │ ├── image-20200703144120242.png │ ├── image-20200703144159048.png │ ├── image-20200703144238614.png │ ├── image-20200703151212485.png │ ├── image-20200703154225724.png │ └── image-20200703154359760.png ├── Bayesian Relational Memory for Semantic Visual Navigation.md ├── Improving Target-driven Visual Navigation with Attention on 3D Spatial Relationships.assets │ ├── image-20200705111106393.png │ ├── image-20200705111939601.png │ ├── image-20200705123909206.png │ ├── image-20200705130626188.png │ ├── image-20200705131656750.png │ ├── image-20200705131744033.png │ ├── image-20200705131747173.png │ ├── image-20200705131859781.png │ ├── image-20200705131926516.png │ └── image-20200705132141819.png ├── Improving Target-driven Visual Navigation with Attention on 3D Spatial Relationships.md ├── Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning.md ├── Learning to learn how to learn Self-adaptive visual navigation using meta-learning.assets │ ├── image-20200702211649011.png │ ├── image-20200702213128415.png │ ├── image-20200703101618018.png │ ├── image-20200703101914142.png │ ├── image-20200703104319711.png │ ├── image-20200703121626950.png │ ├── image-20200703121644122.png │ ├── image-20200703121752526.png │ └── image-20200703121906914.png ├── Learning to learn how to learn Self-adaptive visual navigation using meta-learning.md ├── Long Range Neural Navigation Policies for the Real World.md ├── Scene memory transformer for embodied agents in long-horizon tasks.assets │ ├── image-20200706152627903.png │ ├── image-20200706153724992.png │ ├── image-20200706165235163.png │ ├── image-20200706175359541.png │ ├── image-20200706182841042.png │ ├── image-20200706182939318.png │ ├── image-20200706183146814.png │ ├── image-20200706183211120.png │ └── image-20200706190803028.png ├── Scene memory transformer for embodied agents in long-horizon tasks.md ├── Semi-parametric topological memory for navigation.assets │ ├── image-20200706125057832.png │ ├── image-20200706125603144.png │ ├── image-20200706140848398.png │ ├── image-20200706140947547.png │ └── image-20200706141720545.png ├── Semi-parametric topological memory for navigation.md ├── Target driven visual navigation exploiting object relationships.assets │ ├── image-20200702140946820.png │ ├── image-20200702151238953.png │ ├── image-20200702154153781.png │ ├── image-20200702161215057.png │ ├── image-20200702163714131.png │ ├── image-20200702164548441.png │ └── image-20200702172920825.png ├── Target driven visual navigation exploiting object relationships.md └── Uncertainty-Aware Reinforcement Learning for Collision Avoidance.md ├── Perspective ├── PR Perspective Ⅰ：Embodied AI 的新浪潮.md └── PR Perspective Ⅱ：Robot Learning思考.md ├── Preliminary ├── A Simple Guide for NN.assets │ ├── 20160707204048899.gif │ ├── 853467-20160630141449671-1058672778.png │ ├── 853467-20160630152018906-1524325812.png │ ├── 853467-20160630154317562-311369571.png │ ├── equation-1584587262781.svg │ ├── equation-1584587282205.svg │ ├── equation-1584587725413.svg │ └── equation.svg ├── A Simple Guide for NN.md ├── Preliminary RL basic knowledge.assets │ ├── 131433102201.jpg │ └── 132312526273.jpg ├── Preliminary of RL 1.md ├── Preliminary of RL 2.assets │ ├── 1042406-20180812184148124-1485684702.jpg │ ├── 1560008119444.png │ ├── 201019414696.png │ ├── 201019447506.png │ ├── 201019462191.png │ ├── 221402112851854.png │ ├── 221402155049842.png │ ├── 221402163881216.png │ ├── 221402175506201.png │ ├── interview-14.png │ ├── v2-111ca0554c4504c7aefc9a14d0d92d2f_1440w.jpg │ ├── v2-6c24d01db0b8b94589b2fe6a6efcc7b2_1440w.jpg │ ├── v2-ef32f6901c6a5b8f6eafd8d478ff83ef_1440w.jpg │ ├── v2-f3c12050c797196c7c37b003905a8d30_1440w.jpg │ └── 屏幕快照 2016-01-05 下午9.48.30.png ├── Preliminary of RL 2.md ├── Preliminary of RL 3.assets │ └── equation.svg ├── Preliminary of RL 3.md ├── Preliminary of RL 5.md ├── Reinforcement Learning Notes.assets │ ├── 0_kt9_Z41qxgiI0CDl │ ├── 0_kt9_Z41qxgiI0CDl-1575448739547 │ ├── 0_kt9_Z41qxgiI0CDl-1575448742639 │ ├── 0_kt9_Z41qxgiI0CDl-1575448756568 │ ├── 0_oh-lF13hYWt2Bd6V_ │ ├── 1564474069516.png │ ├── 1564549789614.png │ ├── 76a319586cd215c8f2075b938fc6f6e07c81714b.svg │ ├── 8795d42bd263dcbe55d123e7466b2dd5091490a7.svg │ ├── 9ed1a541005a48d51b624c3b329897064ec2c065.svg │ ├── a325c9e05fa2ccce85eb2384ca00b4888d1c7824.svg │ ├── a5132668c0af8733656505c5fb6c1dff4a7907a1.svg │ ├── dc4621f81a5205e6ae31a35b87c54316e043deda-1575549924223.svg │ ├── dc4621f81a5205e6ae31a35b87c54316e043deda-1575549965545.svg │ ├── dc4621f81a5205e6ae31a35b87c54316e043deda.svg │ ├── image-20191204161236516.png │ ├── image-20191204164022284.png │ ├── image-20191204200910603.png │ ├── image-20191204201005583.png │ ├── image-20191204204627884.png │ ├── image-20191204205459823.png │ ├── image-20191205092649257.png │ ├── image-20191205102211427.png │ ├── image-20191205103234091.png │ ├── image-20191205103636621.png │ ├── image-20191205103810941.png │ ├── image-20191205104741531.png │ ├── image-20191205105318993.png │ ├── image-20191205110708645.png │ ├── image-20191205111303995.png │ ├── image-20191205121930328.png │ └── image-20191205190844049.png ├── Reinforcement Learning Notes.md └── img │ ├── 1558592857137.png │ ├── 1558614556514.png │ ├── 1560008119444.png │ ├── 2019-04-10 19-14-32 的屏幕截图.png │ ├── 2019-04-10 19-17-30 的屏幕截图.png │ ├── 2019-04-10 21-00-18 的屏幕截图.png │ ├── 3-3-1.png │ ├── 3-3-2.png │ ├── 4-1-1-1554948278323.jpg │ ├── 4-1-1.jpg │ ├── 4-5-4.png │ ├── 4155986-e77eec1baba5aeea.webp │ ├── 5-1-1.png │ ├── DQN3.png │ ├── sl4.png │ └── 屏幕快照 2016-01-05 下午9.48.30.png ├── Probabilistic Robotics ├── PR GaussianProcessRegression.assets │ ├── 1_9xMQMnSPnAFkWqIY2jvIpQ.png │ ├── 1_IdGgdrY_n_9_YfkaCh-dag.png │ ├── 1_YAPmNXea5gKoH3uyRrtITQ.png │ ├── 1_zNQg-o-C2JELQFQjEEDrLw.png │ ├── Illustration-of-a-bivariate-Gaussian-distribution-The-marginal-and-joint-probability.png │ ├── Sun, 10 May 2020 143250.png │ ├── image-20200510155719330.png │ ├── v2-3c25a927c217f13a055794377635faaf_1440w.jpg │ └── 微信图片编辑_20200510130051.jpg ├── PR HMC&MH&Gibbs.assets │ ├── 2018-05-09-gibbs-100.png │ ├── gibbssampler-2dnormal1.png │ ├── image__14_.png │ ├── true.jpg │ └── v2-08cb302ac37b757ee390705d822f87f2_1440w.jpg ├── PR HMC&MH&Gibbs.md ├── PR HMM.md ├── PR IS&MCMC.assets │ ├── 1_3nBb4AqcriLcENdpBp4fpQ@2x.png │ ├── 1_AZBh2kDanLoTFmb3yzErGQ@2x.png │ ├── 1_HclnWfZrh7Nzuj2_aHkPCQ.png │ ├── 1_hKQcryMc6fbcS7r-g0sriQ.png │ ├── 20190511000705.png │ ├── 5cdd91aec0102b09bad70aff4bd0e9b2.jpg │ ├── importance_sampling_concept.png │ ├── v2-9514f7703820b5bf99c98405eb413359_1440w.jpg │ └── v2-eb0945aa2185df958f4568e58300e77a_1440w.gif ├── PR IS&MCMC.md ├── PR Ⅰ MLE&MAP.md ├── PR Ⅱ Bayesian ├── PR Ⅱ MCMC&EM.md ├── PR Ⅲ Bayesian_MCMC.assets │ ├── 1_3nBb4AqcriLcENdpBp4fpQ@2x.png │ ├── 1_AZBh2kDanLoTFmb3yzErGQ@2x.png │ ├── Beta_9_7.png │ └── Example-of-Bayesian-inference-with-a-prior-distribution-a-posterior-distribution-and.png ├── PR Ⅲ GaussianProcessRegression.md ├── PR Ⅳ BayesFilter.md ├── PR Ⅳ BayesNeuralNetwork.assets │ ├── bayes_nn.png │ ├── bayesian_statistics.jpg │ └── extrapolation_graph.png ├── PR Ⅳ BayesNeuralNetwork.md ├── PR Ⅴ GMM.assets │ ├── aHR0cDovL2ltZy5ibG9nLmNzZG4ubmV0LzIwMTcwMzAyMTc1NDQyMjcy.jfif │ └── aHR0cDovL2ltZy5ibG9nLmNzZG4ubmV0LzIwMTcwMzAyMTc1NTQ5ODc3.jfif ├── PR Ⅴ GMM.md ├── PR Ⅵ BayesGraph.md ├── PR Ⅶ VariationalInference.md ├── PR Ⅷ MeanField.md ├── PR Ⅸ Entropy.assets │ ├── OIP.jfif │ ├── image-20200519155921660.png │ ├── image-20200523201708073.png │ └── multicolored-abstract-painting-1095624-710x210.jpg ├── PR Ⅸ Entropy.md ├── Probabilistic in Robotic (PR).png └── Probabilistic in Robotic (PR).xmind ├── README.md ├── RL from Demonstration ├── Deep_Q_From_Demonstration.assets │ ├── image-20200522121546661.png │ ├── image-20200522121626682.png │ └── image-20200522122022338.png ├── Deep_Q_From_Demonstration.md ├── RLfrom_Imperfect_Demonstration.assets │ ├── image-20200524205516760.png │ ├── image-20200524213207117.png │ ├── image-20200524215212011.png │ ├── image-20200524215647493.png │ ├── image-20200524221043525.png │ ├── image-20200524221144933.png │ ├── image-20200524221938482.png │ └── image-20200524222429294.png └── RLfrom_Imperfect_Demonstration.md ├── ROS ├── ROS Ⅰ：An Introduction.md ├── ROS Ⅱ：报错解决方案集锦.md ├── ROS Ⅲ：ROS 话题.md ├── ROS Ⅳ：ROS 消息&服务.md └── ROS 机器人实战Ⅰ：TurtleBot3 Simulation SLAM + Navigation.md ├── Reasoning ├── PR Reasoning Ⅰ：Bandit问题与 UCB UCT AlphaGo.md ├── PR Reasoning Ⅱ：Inductive bias 归纳偏置及其在深度学习中的应用.md ├── PR Reasoning Ⅲ：基于图表征的关系推理框架 —— Graph Network.md ├── PR Reasoning Ⅳ：数理逻辑（命题逻辑、谓词逻辑）知识整理.md ├── PR Reasoning Ⅴ：命题推理与First Order Logic Reasoning.md ├── PR Reasoning Ⅵ：Counterfactual Reasoning 反事实推理及其在深度学习中的应用.md ├── PR Reasoning Ⅶ：Graph Reasoning 基于图的推理.md ├── PR Reasoning 序：Reasoning Robotics 推理机器人.md └── Relational inductive biases, deep learning, and graph networks.md ├── Related Works ├── A_survey_on_PS_for_Learning_Robot_controllers_in_a_Handful_of_trials.assets │ ├── image-20200528152451409.png │ ├── image-20200528201113694.png │ ├── image-20200528213920267.png │ ├── image-20200529151635542.png │ ├── image-20200529151707215.png │ └── image-20200529154918336.png ├── A_survey_on_PS_for_Learning_Robot_controllers_in_a_Handful_of_trials.md ├── Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates.md ├── End-to-End Robotic Reinforcement Learning without Reward Engineering.assets │ ├── image-20191208214135640.png │ ├── image-20191209102302915.png │ └── image-20191209104206015.png ├── End-to-End Robotic Reinforcement Learning without Reward Engineering.md ├── IROS2019速读(一).md ├── IROS2019速读(三).assets │ ├── image-20191221113203207.png │ ├── image-20191221114139768.png │ ├── image-20191221114316092.png │ ├── image-20191221200948469.png │ └── image-20191221234256181.png ├── IROS2019速读(三).md ├── IROS2019速读(二).assets │ └── image-20191219185107956.png ├── IROS2019速读(二).md ├── IROS2019速读(五).md ├── IROS2019速读(四).assets │ ├── dream_2.gif │ ├── dream_6.gif │ ├── image-20191222112555208.png │ ├── image-20191222123748477.png │ └── image-20191222144804275.png ├── IROS2019速读(四).md ├── IROS2019速读.assets │ ├── image-20191216203301798.png │ ├── image-20191216211111079.png │ ├── image-20191216212441606.png │ └── image-20191217203423494.png ├── Meta learning An Introduction.assets │ ├── 1_AcaPiikZErVv_iFJzWekQg.gif │ ├── NTM.png │ ├── combine-slow-fast-weights.png │ ├── equation-1577535254476.svg │ ├── equation.svg │ ├── few-shot-classification.png │ ├── image-20191226194740259.png │ ├── image-20191226200939808.png │ ├── image-20191226202123590.png │ ├── image-20191226202326065.png │ ├── lstm-meta-learner.png │ ├── maml-algo.png │ ├── maml.png │ ├── mann-meta-learning.png │ ├── matching-networks.png │ ├── meta-network.png │ ├── prototypical-networks-1577419209148.png │ ├── prototypical-networks.png │ ├── relation-network.png │ ├── reptile-algo.png │ ├── reptile_vs_FOMAML.png │ ├── siamese-conv-net.png │ ├── train-meta-learner.png │ └── v2-2d61ff11eb1a5a9e52d6c12eb333eb4b_hd.jpg ├── Meta learning An Introduction.md ├── Meta-Reinforcement-Learning An Introduction.md ├── Overcoming Exploration in Reinforcement Learning with Demonstrations.assets │ └── image-20191211211229554.png ├── Overcoming Exploration in Reinforcement Learning with Demonstrations.md ├── The Predictron End-To-End Learning and Planning.assets │ ├── image-20191211163322741.png │ ├── image-20191211201710251.png │ ├── image-20191211202507981.png │ ├── image-20191212102538657.png │ └── image-20191212102714873.png ├── The Predictron End-To-End Learning and Planning.md ├── When to Trust Your Model Model-Based Policy Optimization.assets │ └── image-20191215201141993.png ├── When to Trust Your Model Model-Based Policy Optimization.md ├── 智源大会笔记.assets │ ├── image-20200624101204649.png │ ├── image-20200624101305253.png │ ├── image-20200624101402324.png │ ├── image-20200624101416544.png │ ├── image-20200624101704078.png │ ├── image-20200624101836768.png │ ├── image-20200624102016044.png │ ├── image-20200624102239093.png │ ├── image-20200624102756731.png │ ├── image-20200624102845779.png │ ├── image-20200624102945812.png │ ├── image-20200624103018972.png │ ├── image-20200624103109295.png │ ├── image-20200624103145785.png │ ├── image-20200624104915752.png │ ├── image-20200624105312169.png │ ├── image-20200624105434860.png │ ├── image-20200624105534892.png │ ├── image-20200624110446944.png │ └── image-20200624110634783.png └── 智源大会笔记.md ├── Representation └── Repre 1：Introduction.md ├── Robotics └── Bimanual coordination.md ├── Simulator ├── MuJoCo机器人建模教程.assets │ ├── download.html │ ├── grid1.png │ ├── grid2.png │ ├── grid2pin.png │ ├── openai-robotics-hand-with-cube-solved-crop-2000w.jpg │ ├── particle2.png │ └── unnamed.png ├── MuJoCo机器人建模教程.md ├── MuJoCo详细使用指南.md ├── PyBullet详细使用指南.md └── Sim2real.md ├── Structured ├── PR Structure Ⅱ .assets │ ├── image-20200719135624489.png │ ├── image-20200719135706275.png │ ├── image-20200719141215552.png │ ├── image-20200719141544422.png │ ├── image-20200719141645589.png │ ├── image-20200719142143932.png │ ├── image-20200719160240612.png │ ├── image-20200719160620909.png │ ├── image-20200719161117095.png │ ├── image-20200719161134980.png │ ├── image-20200719163653512.png │ ├── image-20200719164027556.png │ ├── image-20200719164332203.png │ └── image-20200719164745855.png ├── PR Structured Ⅰ GNN.assets │ ├── image-20200712132319666.png │ ├── image-20200712152008297.png │ ├── image-20200712152040834.png │ ├── image-20200712152050882.png │ ├── image-20200712154103209.png │ ├── image-20200712155738229.png │ ├── image-20200712161616515.png │ ├── image-20200712165321733.png │ ├── image-20200712170431243.png │ ├── image-20200712171214741.png │ ├── image-20200712172004136.png │ ├── image-20200712172227154.png │ ├── image-20200712174617755.png │ ├── image-20200712175300499.png │ ├── image-20200712175439303.png │ ├── image-20200712175812940.png │ ├── image-20200712180112851.png │ ├── image-20200712195813501.png │ ├── image-20200712203453039.png │ ├── image-20200712212800180.png │ ├── image-20200712212854141.png │ ├── image-20200712213349638.png │ ├── image-20200712213614662.png │ ├── image-20200712214932503.png │ ├── image-20200712215137652.png │ ├── image-20200712230809276.png │ ├── image-20200712231606429.png │ └── image-20200719135706275.png ├── PR Structured Ⅱ：Structured Probabilistic Model Ⅰ.md ├── PR Structured Ⅲ：马尔可夫、隐马尔可夫 HMM 、条件随机场 CRF 全解析及其python实现.md ├── PR Structured Ⅳ：General Conditional Random Field (CRF).md ├── PR Structured Ⅴ：GraphRNN——依次生成节点和边的图生成模型.md └── PR StructuredⅠ：Graph Neural Network An Introduction .md ├── Tools ├── Atlas │ ├── Atlas 使用指南.assets │ │ └── Atlas软硬件架构.png │ ├── Atlas 使用指南.md │ ├── Atlas安装配置流程.eps │ ├── Atlas安装配置流程.png │ ├── Atlas软硬件架构.eps │ └── Atlas软硬件架构.png ├── C++部署Pytorch模型方法.docx ├── Docker │ ├── Docker Ⅰ：安装与测试指南.md │ ├── Docker Ⅱ：管理与使用命令手册.md │ ├── Docker Ⅲ：Nvidia Docker安装与测试指南.md │ ├── Docker Ⅳ：Nvidia Docker使用命令手册.md │ └── Docker Ⅴ：Docker与Nvidia Docker踩坑与解决方案记录集.md ├── Habitat │ └── Habitat Challenge提交指南.md ├── Tools 1：Qt 转 PyQt5 的 Pycharm 插件.md ├── Tools 3：python socket 服务器与客户端双向通信.md ├── Tools 4：Python三行转并行——真香.md ├── Tools 5：Python三行转并行后续——全局变量.md ├── Tools 6：如何用Readthedoc写一份优雅的技术文档.md ├── Tools 7：Python颜色设置.md ├── Tools 8：Tex符号大全.md ├── Tools 9：Zotero使用指南.md ├── Ubuntu │ └── Ubuntu系统问题.md └── color.py ├── Utils ├── HTML2PDF.py ├── PDFselector.py └── basic_plot.py └── img └── image-20230825121432059.png /.gitattributes: -------------------------------------------------------------------------------- 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