# Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions

arXiv: https://arxiv.org/abs/2509.12741 · HTML: https://mlsanity.com/paper/2509.12741/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 44 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://mlsanity.com/paper/2312.00863/) (2023)
- [Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1810.10191/) (2018)
- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation](https://mlsanity.com/paper/2211.09423/) (2022)
- [See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation](https://mlsanity.com/paper/2212.03858/) (2022)
- [Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing](https://mlsanity.com/paper/2312.01853/) (2023)
- [Assistive Gym: A Physics Simulation Framework for Assistive Robotics](https://mlsanity.com/paper/1910.04700/) (2019)
- [VIRDO: Visio-tactile Implicit Representations of Deformable Objects](https://mlsanity.com/paper/2202.00868/) (2022)
- [Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning](https://mlsanity.com/paper/2205.14850/) (2022)
- [Real-World Offline Reinforcement Learning from Vision Language Model Feedback](https://mlsanity.com/paper/2411.05273/) (2024)
- [Visuotactile Affordances for Cloth Manipulation with Local Control](https://mlsanity.com/paper/2212.05108/) (2022)
- [One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments](https://mlsanity.com/paper/2306.12372/) (2023)
- [Learning to Collaborate from Simulation for Robot-Assisted Dressing](https://mlsanity.com/paper/1909.06682/) (2019)
- [Multi-Modal Geometric Learning for Grasping and Manipulation](https://mlsanity.com/paper/1803.07671/) (2018)
- [Simulation of Vision-based Tactile Sensors using Physics based Rendering](https://mlsanity.com/paper/2012.13184/) (2020)
- [Learning Visuo-Haptic Skewering Strategies for Robot-Assisted Feeding](https://mlsanity.com/paper/2211.14648/) (2022)
- [Deep Haptic Model Predictive Control for Robot-Assisted Dressing](https://mlsanity.com/paper/1709.09735/) (2017)
- [Force-Constrained Visual Policy: Safe Robot-Assisted Dressing via Multi-Modal Sensing](https://mlsanity.com/paper/2311.04390/) (2023)
- [ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces](https://mlsanity.com/paper/2405.04549/) (2024)

## Built on by (most cited first, 2 of 2)

- [GenPHRI: Agentic Generative Simulation for Physical Human-Robot Interaction](https://mlsanity.com/paper/2604.08664/) (2026)
- [Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing](https://mlsanity.com/paper/2609.04759/) (2026)

## Abstract (arXiv)

Robot-assisted dressing has the potential to significantly improve the lives of individuals with mobility impairments. To ensure an effective and comfortable dressing experience, the robot must be able to handle challenging deformable garments, apply appropriate forces, and adapt to limb movements throughout the dressing process. Prior work often makes simplifying assumptions -- such as static human limbs during dressing -- which limits real-world applicability. In this work, we develop a robot-assisted dressing system capable of handling partial observations with visual occlusions, as well as robustly adapting to arm motions during the dressing process. Given a policy trained in simulation with partial observations, we propose a method to fine-tune it in the real world using a small amount of data and multi-modal feedback from vision and force sensing, to further improve the policy's adaptability to arm motions and enhance safety. We evaluate our method in simulation with simplified articulated human meshes and in a real world human study with 12 participants across 264 dressing trials. Our policy successfully dresses two long-sleeve everyday garments onto the participants while being adaptive to various kinds of arm motions, and greatly outperforms prior baselines in terms of task completion and user feedback. Video are available at https://dressing-motion.github.io/.