{"id":55236,"url":"https://github.com/Phylliade/awesome-machine-learning-robotics","name":"awesome-machine-learning-robotics","description":"A curated list of resources about Machine Learning for Robotics","projects_count":39,"last_synced_at":"2026-06-05T22:00:21.806Z","repository":{"id":79447684,"uuid":"93535099","full_name":"Phylliade/awesome-machine-learning-robotics","owner":"Phylliade","description":"A curated list of resources about Machine Learning for Robotics","archived":false,"fork":false,"pushed_at":"2019-08-26T13:56:55.000Z","size":12,"stargazers_count":376,"open_issues_count":0,"forks_count":54,"subscribers_count":6,"default_branch":"master","last_synced_at":"2026-05-20T09:46:42.804Z","etag":null,"topics":["approximate-inference","awesome","awesome-list","curriculum-learning","deep-learning","deep-reinforcement-learning","machine-learning","reinforcement-learning","robotics"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Phylliade.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-06-06T15:38:41.000Z","updated_at":"2026-05-16T17:59:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"49252f9e-5eae-4ced-8448-8a92baa5f1d8","html_url":"https://github.com/Phylliade/awesome-machine-learning-robotics","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Phylliade/awesome-machine-learning-robotics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Phylliade%2Fawesome-machine-learning-robotics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Phylliade%2Fawesome-machine-learning-robotics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Phylliade%2Fawesome-machine-learning-robotics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Phylliade%2Fawesome-machine-learning-robotics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Phylliade","download_url":"https://codeload.github.com/Phylliade/awesome-machine-learning-robotics/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Phylliade%2Fawesome-machine-learning-robotics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33961252,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-05T02:00:06.157Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-24T03:14:53.385Z","updated_at":"2026-06-05T22:00:21.806Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Meta-Learning","Reinforcement Learning","Learned Approximate Inference","Robotic platforms","Implementing RL algorithms","Robotic simulator"],"sub_categories":["Multi-task learning","Deep Reinforcement Learning","Reinforcement Learning Theory","Reproducibility of Deep RL experiments","Policy Search","Goal Exploration Processes","Curriculum learning"],"readme":"# Awesome Machine Learning for Robotics [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\n\nMachine Learning is becoming a common technique to address robotics tasks. This repository intends cover this usage from a broad point of view.\n\n\n# Papers\n## Learned Approximate Inference\n* [Auto-Encoding Variational Bayes](http://arxiv.org/abs/1312.6114), by Kingma et Al.\n* [Stochastic Backpropagation and Approximate Inference in Deep Generative Models](https://arxiv.org/abs/1401.4082), by Rezende et Al.\n* [Variational Inference with Normalizing Flows](https://arxiv.org/pdf/1505.05770v6.pdf), by Rezende et Al.\n* [Improved Variational Inference with Inverse Autoregressive Flows](https://fr.arxiv.org/pdf/1706.02326), By Kingma et Al.\n* [Gradient Estimation Using Stochastic Computation Graphs](https://arxiv.org/abs/1506.05254) by Schulman et Al.\n* [Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data](https://arxiv.org/abs/1605.06432) by Karl et Al.\n* [Deep Kalman Filters](https://arxiv.org/abs/1511.05121) by Krishnan et Al.\n* [Variational Inference: Foundations and Modern Methods](http://www.cs.columbia.edu/~blei/talks/2016_NIPS_VI_tutorial.pdf), byu Blei et Al.\n\n## Reinforcement Learning\n### Deep Reinforcement Learning\n* Deep Q-Learning: [Human-level control through deep reinforcement learning](https://www.nature.com/nature/journal/v518/n7540/full/nature14236.html), by Mnih et Al.\n* DDPG: [Continuous control with Deep Reinforcement Learning](https://arxiv.org/abs/1509.02971), by Lillicrap et Al.\n* [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952), by Schaul et Al.\n* Auxiliary tasks: [Reinforcement learning with unsupervised auxiliary tasks](https://deepmind.com/blog/reinforcement-learning-unsupervised-auxiliary-tasks/), by Jaderberg et Al.\n* [Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/abs/1707.02286), by Heess et Al.\n\n### Reproducibility of Deep RL experiments\n* [Deep RL that matters](https://arxiv.org/abs/1709.06560), by Henderson et Al.\n* [ Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://arxiv.org/abs/1708.04133), by Islam et Al.\n\n### Reinforcement Learning Theory\n* REINFORCE: [Simple statistical gradient-following algorithms for connectionist reinforcement learning](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf), Williams et Al.\n* Policy Gradient Theorem: [Policy Gradient Methods for Reinforcement Learning with Function Approximation](https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf), Sutton et Al.\n* [Deterministic Policy Gradient Algorithms](http://proceedings.mlr.press/v32/silver14.pdf), Silver et Al.\n\n### Policy Search\n* Policy search suvery: [Reinforcement learning of motor skills with policy gradients](http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Neural-Netw-2008-21-682_4867%5b0%5d.pdf), by Peters and Schaal.\n* [Guided policy search](https://graphics.stanford.edu/projects/gpspaper/gps_full.pdf), by Levine et Al.\n\n## Meta-Learning\n### Goal Exploration Processes\n* [Intrinsically Motivated Multi-Task Reinforcement Learning](https://www.reddit.com/r/MachineLearning/comments/5q9fnr/d_intrinsically_motivated_multitask_reinforcement/), by Forestier and Oudeyer.\n### Curriculum learning\n* [Automated curriculum learning](https://arxiv.org/abs/1704.03003), by Graves et Al.\n### Multi-task learning\n* [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400), by Finn and Al.\n\n\n\n# Implementations\n* [OpenAI Gym](https://gym.openai.com/): A Python library providing many simulation environments.\n* [OpenAI baselines](https://github.com/openai/baselines): Implementations of Deep Reinforcement Learning algorithms by experts.\n* [Explauto](https://github.com/flowersteam/explauto): A library to perform intrinsically motivated exploration.\n* [Guided Policy Search](http://rll.berkeley.edu/gps/): Implementation of the Guided Policy Search algorithm.\n* [Keras-RL](https://github.com/matthiasplappert/keras-rl): A keras-compatible Deep Reinforcement Learning framework (DQN, SARSA, DDPG...).\n* [Deepmind DQN](https://github.com/deepmind/dqn): Deepmind's implementation used for the [Nature paper](https://www.nature.com/nature/journal/v518/n7540/full/nature14236.html).\n* [Devsisters DQN](https://github.com/devsisters/DQN-tensorflow): A nice DQN implementation.\n\n## Implementing RL algorithms\n* [Best pratices in implementing Deep RL algorithms](https://blog.openai.com/openai-baselines-dqn/), as part of a blog post.\n* [A note about gradient clipping](https://github.com/devsisters/DQN-tensorflow/issues/16), by Karpathy. Further explained in a [blog post](https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b).\n\n## Robotic simulator\n* [MuJoCo](http://www.mujoco.org/): The reference. Closed-source\n* [OpenAI Roboschool](https://github.com/openai/roboschool): A Mujoco clone in bullet, open-source.\n* [Gazebo](http://gazebosim.org/): A simulator used in the ROS suite.\n* [V-REP](http://www.coppeliarobotics.com/): A simulator used with the Poppy project.\n\n## Robotic platforms\n* [Poppy](https://www.poppy-project.org/en/): An open-source 3D-printed robotic ecosystem (humanoid, torso...)\n\n# About\n\nAuthors:\n* [Alexandre Pere](https://www.linkedin.com/in/alexandre-pere-432b6883/)\n* [Pierre Manceron](https://www.linkedin.com/in/pierre-manceron-a136b538/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/phylliade%2Fawesome-machine-learning-robotics/projects"}