# Assistive Gym: A Physics Simulation Framework for Assistive Robotics

arXiv: https://arxiv.org/abs/1910.04700 · HTML: https://mlsanity.com/paper/1910.04700/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 30 of 44)

- [Causal Confusion and Reward Misidentification in Preference-Based Reward Learning](https://mlsanity.com/paper/2204.06601/) (2022)
- [DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools](https://mlsanity.com/paper/2203.17275/) (2022)
- [Interesting Object, Curious Agent: Learning Task-Agnostic Exploration](https://mlsanity.com/paper/2111.13119/) (2021)
- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [Context-Aware Safe Reinforcement Learning for Non-Stationary Environments](https://mlsanity.com/paper/2101.00531/) (2021)
- [Unsupervised Learning of Visual 3D Keypoints for Control](https://mlsanity.com/paper/2106.07643/) (2021)
- [PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos](https://mlsanity.com/paper/2404.04430/) (2024)
- [ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment](https://mlsanity.com/paper/2308.09987/) (2023)
- [Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models](https://mlsanity.com/paper/2301.04741/) (2023)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning](https://mlsanity.com/paper/2110.06539/) (2021)
- [Learning Representations that Enable Generalization in Assistive Tasks](https://mlsanity.com/paper/2212.03175/) (2022)
- [Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning](https://mlsanity.com/paper/2103.04616/) (2021)
- [EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics](https://mlsanity.com/paper/2209.08996/) (2022)
- [ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2202.02465/) (2022)
- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
- [Functional optimal transport: map estimation and domain adaptation for functional data](https://mlsanity.com/paper/2102.03895/) (2021)
- [Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions](https://mlsanity.com/paper/2208.05632/) (2022)
- [A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions](https://mlsanity.com/paper/2412.17377/) (2024)
- [DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning](https://mlsanity.com/paper/2402.15957/) (2024)
- [DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI](https://mlsanity.com/paper/2108.12536/) (2021)
- [Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?](https://mlsanity.com/paper/2312.17168/) (2023)
- [Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions](https://mlsanity.com/paper/2509.12741/) (2025)
- [Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2107.03685/) (2021)
- [Quantifying Assistive Robustness Via the Natural-Adversarial Frontier](https://mlsanity.com/paper/2310.10610/) (2023)
- ["Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration](https://mlsanity.com/paper/2310.18791/) (2023)

## Abstract (arXiv)

Autonomous robots have the potential to serve as versatile caregivers that improve quality of life for millions of people worldwide. Yet, conducting research in this area presents numerous challenges, including the risks of physical interaction between people and robots. Physics simulations have been used to optimize and train robots for physical assistance, but have typically focused on a single task. In this paper, we present Assistive Gym, an open source physics simulation framework for assistive robots that models multiple tasks. It includes six simulated environments in which a robotic manipulator can attempt to assist a person with activities of daily living (ADLs): itch scratching, drinking, feeding, body manipulation, dressing, and bathing. Assistive Gym models a person's physical capabilities and preferences for assistance, which are used to provide a reward function. We present baseline policies trained using reinforcement learning for four different commercial robots in the six environments. We demonstrate that modeling human motion results in better assistance and we compare the performance of different robots. Overall, we show that Assistive Gym is a promising tool for assistive robotics research.