# DoorGym: A Scalable Door Opening Environment And Baseline Agent

arXiv: https://arxiv.org/abs/1908.01887 · HTML: https://mlsanity.com/paper/1908.01887/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 26 of 26)

- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations](https://mlsanity.com/paper/2107.14483/) (2021)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [Multi-skill Mobile Manipulation for Object Rearrangement](https://mlsanity.com/paper/2209.02778/) (2022)
- [PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations](https://mlsanity.com/paper/2303.16958/) (2023)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)
- [Improving Policy Optimization with Generalist-Specialist Learning](https://mlsanity.com/paper/2206.12984/) (2022)
- [Harmonic Mobile Manipulation](https://mlsanity.com/paper/2312.06639/) (2023)
- [Learning to Open and Traverse Doors with a Legged Manipulator](https://mlsanity.com/paper/2409.04882/) (2024)
- [Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer](https://mlsanity.com/paper/2512.01061/) (2025)
- [AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning](https://mlsanity.com/paper/2502.11124/) (2025)
- [Part-Guided 3D RL for Sim2Real Articulated Object Manipulation](https://mlsanity.com/paper/2404.17302/) (2024)
- [SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects](https://mlsanity.com/paper/2312.01307/) (2023)
- [Geometric Red-Teaming for Robotic Manipulation](https://mlsanity.com/paper/2509.12379/) (2025)
- [Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation](https://mlsanity.com/paper/2206.06289/) (2022)
- [H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions](https://mlsanity.com/paper/2210.12521/) (2022)
- [Adaptive Articulated Object Manipulation On The Fly with Foundation Model Reasoning and Part Grounding](https://mlsanity.com/paper/2507.18276/) (2025)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Single RGB-D Camera Teleoperation for General Robotic Manipulation](https://mlsanity.com/paper/2106.14396/) (2021)
- [Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing](https://mlsanity.com/paper/2605.15352/) (2026)

## Abstract (arXiv)

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/