中国大学MOOC《机器人操作系统入门》课程代码示例

PARL A high-performance distributed training framework for Reinforcement Learning

A toolkit for robot learning research.

Reinforcement Learning Model Zoo with TensorLayer

Detect 6-DOF grasp poses in point clouds

Robot Learning of Shifting Objects for Grasping in Cluttered Environments

YOLO reproduce summary (now based on YOLOv3)

Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)

A large-scale benchmark and learning environment.

Multi-Viewpoint Picking (ICRA 2019)

《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

NAF(Continuous DQN)でPendulum-v0、Reacher-v2、FetchReach-1を学習/Testするサンプルコード。

A simple, fully convolutional model for real-time instance segmentation.

Simple, straighforward ROS library for hand-eye calibration

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

CRAVES: Controlling Robotic Arm with a Vision-based, Economic System

Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)

Integrating learning and task planning for robots with Keras, including simulation, real robot, and multiple dataset support.

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo

A memory-efficient implementation of DenseNets

[RSS 2019] End-to-End Robotic Reinforcement Learning without Reward Engineering

ROS package for dynamic obstacle avoidance for ground robots trained with deep RL

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning (ICML 2019)

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.