https://github.com/Phylliade/awesome-machine-learning-robotics

A curated list of resources about Machine Learning for Robotics
https://github.com/Phylliade/awesome-machine-learning-robotics

List: awesome-machine-learning-robotics

approximate-inference awesome awesome-list curriculum-learning deep-learning deep-reinforcement-learning machine-learning reinforcement-learning robotics

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A curated list of resources about Machine Learning for Robotics

• Host: GitHub
• URL: https://github.com/Phylliade/awesome-machine-learning-robotics
• Owner: Phylliade
• Created: 2017-06-06T15:38:41.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-08-26T13:56:55.000Z (about 5 years ago)
• Last Synced: 2024-04-11T03:26:49.714Z (7 months ago)
• Topics: approximate-inference, awesome, awesome-list, curriculum-learning, deep-learning, deep-reinforcement-learning, machine-learning, reinforcement-learning, robotics
• Homepage:
• Size: 11.7 KB
• Stars: 114
• Watchers: 8
• Forks: 19
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• ultimate-awesome - awesome-machine-learning-robotics - A curated list of resources about Machine Learning for Robotics. (Other Lists / PowerShell Lists)

README

        
# Awesome Machine Learning for Robotics [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/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

* [Auto-Encoding Variational Bayes](http://arxiv.org/abs/1312.6114), by Kingma et Al.

* [Stochastic Backpropagation and Approximate Inference in Deep Generative Models](https://arxiv.org/abs/1401.4082), by Rezende et Al.

* [Variational Inference with Normalizing Flows](https://arxiv.org/pdf/1505.05770v6.pdf), by Rezende et Al.

* [Improved Variational Inference with Inverse Autoregressive Flows](https://fr.arxiv.org/pdf/1706.02326), By Kingma et Al.

* [Gradient Estimation Using Stochastic Computation Graphs](https://arxiv.org/abs/1506.05254) by Schulman et Al.

* [Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data](https://arxiv.org/abs/1605.06432) by Karl et Al.

* [Deep Kalman Filters](https://arxiv.org/abs/1511.05121) by Krishnan et Al.

* [Variational Inference: Foundations and Modern Methods](http://www.cs.columbia.edu/~blei/talks/2016_NIPS_VI_tutorial.pdf), byu Blei et Al.

## Reinforcement Learning

### Deep Reinforcement Learning

* 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.

* DDPG: [Continuous control with Deep Reinforcement Learning](https://arxiv.org/abs/1509.02971), by Lillicrap et Al.

* [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952), by Schaul et Al.

* Auxiliary tasks: [Reinforcement learning with unsupervised auxiliary tasks](https://deepmind.com/blog/reinforcement-learning-unsupervised-auxiliary-tasks/), by Jaderberg et Al.

* [Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/abs/1707.02286), by Heess et Al.

### Reproducibility of Deep RL experiments

* [Deep RL that matters](https://arxiv.org/abs/1709.06560), by Henderson et Al.

* [ Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://arxiv.org/abs/1708.04133), by Islam et Al.

### Reinforcement Learning Theory

* REINFORCE: [Simple statistical gradient-following algorithms for connectionist reinforcement learning](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf), Williams et Al.

* 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.

* [Deterministic Policy Gradient Algorithms](http://proceedings.mlr.press/v32/silver14.pdf), Silver et Al.

### Policy Search

* 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.

* [Guided policy search](https://graphics.stanford.edu/projects/gpspaper/gps_full.pdf), by Levine et Al.

## Meta-Learning

### Goal Exploration Processes

* [Intrinsically Motivated Multi-Task Reinforcement Learning](https://www.reddit.com/r/MachineLearning/comments/5q9fnr/d_intrinsically_motivated_multitask_reinforcement/), by Forestier and Oudeyer.

### Curriculum learning

* [Automated curriculum learning](https://arxiv.org/abs/1704.03003), by Graves et Al.

### Multi-task learning

* [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400), by Finn and Al.

# Implementations

* [OpenAI Gym](https://gym.openai.com/): A Python library providing many simulation environments.

* [OpenAI baselines](https://github.com/openai/baselines): Implementations of Deep Reinforcement Learning algorithms by experts.

* [Explauto](https://github.com/flowersteam/explauto): A library to perform intrinsically motivated exploration.

* [Guided Policy Search](http://rll.berkeley.edu/gps/): Implementation of the Guided Policy Search algorithm.

* [Keras-RL](https://github.com/matthiasplappert/keras-rl): A keras-compatible Deep Reinforcement Learning framework (DQN, SARSA, DDPG...).

* [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).

* [Devsisters DQN](https://github.com/devsisters/DQN-tensorflow): A nice DQN implementation.

## Implementing RL algorithms

* [Best pratices in implementing Deep RL algorithms](https://blog.openai.com/openai-baselines-dqn/), as part of a blog post.

* [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).

## Robotic simulator

* [MuJoCo](http://www.mujoco.org/): The reference. Closed-source

* [OpenAI Roboschool](https://github.com/openai/roboschool): A Mujoco clone in bullet, open-source.

* [Gazebo](http://gazebosim.org/): A simulator used in the ROS suite.

* [V-REP](http://www.coppeliarobotics.com/): A simulator used with the Poppy project.

## Robotic platforms

* [Poppy](https://www.poppy-project.org/en/): An open-source 3D-printed robotic ecosystem (humanoid, torso...)

# About

Authors:

* [Alexandre Pere](https://www.linkedin.com/in/alexandre-pere-432b6883/)

* [Pierre Manceron](https://www.linkedin.com/in/pierre-manceron-a136b538/)