DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo

2022-05-17Code Available0· sign in to hype

Woong Gyu La, Lingjie Kong, Sunil Muralidhara, Pratik Nichat

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Code

github.com/juhgiyo/deepsim
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Abstract

We propose DeepSim, a reinforcement learning environment build toolkit for ROS and Gazebo. It allows machine learning or reinforcement learning researchers to access the robotics domain and create complex and challenging custom tasks in ROS and Gazebo simulation environments. This toolkit provides building blocks of advanced features such as collision detection, behaviour control, domain randomization, spawner, and many more. DeepSim is designed to reduce the boundary between robotics and machine learning communities by providing Python interface. In this paper, we discuss the components and design decisions of DeepSim Toolkit.

Tasks

BIG-bench Machine Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

DeepSim: A Reinforcement Learning Environment Build Toolkit for ROS and Gazebo

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Abstract

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