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arXiv · 1908.01887

DoorGym: A Scalable Door Opening Environment And Baseline Agent

Yusuke Urakami·Alec Hodgkinson·Casey Carlin·Randall Leu·Luca Rigazio·Pieter Abbeel
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Abstract

In order to practically implement the door opening task, a policy ought to be robust to a wide distribution of door types and environment settings. Reinforcement Learning (RL) with Domain Randomization (DR) is a promising technique to enforce policy generalization, however, there are only a few accessible training environments that are inherently designed to train agents in domain randomized environments. We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy. We intend for our environment to lie at the intersection of domain transfer, practical tasks, and realism. We also provide baseline Proximal Policy Optimization and Soft Actor-Critic implementations, which achieves success rates between 0% up to 95% for opening various types of doors in this environment. Moreover, the real-world transfer experiment shows the trained policy is able to work in the real world. Environment kit available here: https://github.com/PSVL/DoorGym/

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BibTeXRIS

Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, Pieter Abbeel. 2019-08-05. DoorGym: A Scalable Door Opening Environment And Baseline Agent. https://arxiv.org/abs/1908.01887

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