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Awesome Machine Learning for Robotics Awesome

Machine Learning is becoming a common technique to address robotics tasks. This repository intends cover this usage from a broad point of view.

Papers

Learned Approximate Inference

Reinforcement Learning

Deep Reinforcement Learning

Reproducibility of Deep RL experiments

Reinforcement Learning Theory

Policy Search

Meta-Learning

Goal Exploration Processes

Curriculum learning

Multi-task learning

Implementations

  • OpenAI Gym: A Python library providing many simulation environments.
  • OpenAI baselines: Implementations of Deep Reinforcement Learning algorithms by experts.
  • Explauto: A library to perform intrinsically motivated exploration.
  • Guided Policy Search: Implementation of the Guided Policy Search algorithm.
  • Keras-RL: A keras-compatible Deep Reinforcement Learning framework (DQN, SARSA, DDPG...).
  • Deepmind DQN: Deepmind's implementation used for the Nature paper.
  • Devsisters DQN: A nice DQN implementation.

Implementing RL algorithms

Robotic simulator

  • MuJoCo: The reference. Closed-source
  • OpenAI Roboschool: A Mujoco clone in bullet, open-source.
  • Gazebo: A simulator used in the ROS suite.
  • V-REP: A simulator used with the Poppy project.

Robotic platforms

  • Poppy: An open-source 3D-printed robotic ecosystem (humanoid, torso...)

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