DoorGym: A Scalable Door Opening Environment And Baseline Agent
Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca, Rigazio, Pieter Abbeel

TL;DR
DoorGym is an open-source simulation environment that uses domain randomization to train robust door-opening policies, demonstrating successful transfer from simulation to real-world applications.
Contribution
We introduce DoorGym, a scalable simulation framework for training door-opening policies with domain randomization, and provide baseline RL implementations with real-world transfer capability.
Findings
Baseline policies achieve up to 95% success rate in simulation.
Trained policies successfully transfer to real-world door opening.
DoorGym enables robust policy training across diverse door types.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsReinforcement Learning in Robotics · Software Engineering Research · Robot Manipulation and Learning
MethodsEntropy Regularization · Proximal Policy Optimization
