# Dressing in Motion: A Human Motion-Aware Diffusion Policy for Robot-Assisted Dressing

arXiv: https://arxiv.org/abs/2609.04759 · HTML: https://mlsanity.com/paper/2609.04759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 4 Sep 2026.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 25 of 25)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities](https://mlsanity.com/paper/2604.15483/) (2026)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [Self-Supervised Correspondence in Visuomotor Policy Learning](https://mlsanity.com/paper/1909.06933/) (2019)
- [Affordance-based Robot Manipulation with Flow Matching](https://mlsanity.com/paper/2409.01083/) (2024)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [Assistive Gym: A Physics Simulation Framework for Assistive Robotics](https://mlsanity.com/paper/1910.04700/) (2019)
- [Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2312.10008/) (2023)
- [One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments](https://mlsanity.com/paper/2306.12372/) (2023)
- [Unfolding the Literature: A Review of Robotic Cloth Manipulation](https://mlsanity.com/paper/2407.01361/) (2024)
- [Learning to Collaborate from Simulation for Robot-Assisted Dressing](https://mlsanity.com/paper/1909.06682/) (2019)
- [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)
- [Learning Task-Parameterized Skills from Few Demonstrations](https://mlsanity.com/paper/2201.09975/) (2022)
- [Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme](https://mlsanity.com/paper/2301.02749/) (2023)
- [Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps](https://mlsanity.com/paper/2404.13458/) (2024)
- [Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions](https://mlsanity.com/paper/2509.12741/) (2025)
- [Preference Aligned Visuomotor Diffusion Policies for Deformable Object Manipulation](https://mlsanity.com/paper/2602.09583/) (2026)
- [Close-Fitting Dressing Assistance Based on State Estimation of Feet and Garments with Semantic-based Visual Attention](https://mlsanity.com/paper/2505.03400/) (2025)
- [Evaluating the Pre-Dressing Step: Unfolding Medical Garments Via Imitation Learning](https://mlsanity.com/paper/2507.18436/) (2025)
- [Bimanual Robot-Assisted Dressing: A Spherical Coordinate-Based Strategy for Tight-Fitting Garments](https://mlsanity.com/paper/2508.12274/) (2025)
- [Object-centric Task Representation and Transfer using Diffused Orientation Fields](https://mlsanity.com/paper/2511.18563/) (2025)

## Abstract (arXiv)

Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human motion remains challenging, as complex garment--human contact and occlusions make it difficult to generate actions aligned with arm movements. In this letter, we propose a visuomotor policy that learns dressing skills from static expert demonstrations and generalizes to dynamic user-motion scenarios. A diffusion policy tailored to garment--human interaction geometry learns from partially observed point clouds with varied arm postures. We then introduce an object-centric representation based on PDE diffusion to capture the axial distribution of the arm. By sampling motion-relevant regions and registering them across consecutive observations, the proposed method approximates arm motion and reactively adapts the executed trajectory. We evaluate our method in simulation and a real-world human study involving nine participants, three garment types, and six arm-motion patterns. Results show that our method outperforms baselines in dressing progress, freedom of movement, and user comfort. The project website is https://anonymous.4open.science/w/dressing-in-motion.