# GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings

arXiv: https://arxiv.org/abs/2501.17855 · HTML: https://mlsanity.com/paper/2501.17855/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 71 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RCareWorld: A Human-centric Simulation World for Caregiving Robots](https://mlsanity.com/paper/2210.10821/) (2022)
- [Learning Representations that Enable Generalization in Assistive Tasks](https://mlsanity.com/paper/2212.03175/) (2022)
- [FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishes](https://mlsanity.com/paper/2407.07561/) (2024)
- [Feel the Bite: Robot-Assisted Inside-Mouth Bite Transfer using Robust Mouth Perception and Physical Interaction-Aware Control](https://mlsanity.com/paper/2403.04067/) (2024)
- [Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme](https://mlsanity.com/paper/2301.02749/) (2023)
- [Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups](https://mlsanity.com/paper/2207.03348/) (2022)
- [SPARCS: Structuring Physically Assistive Robotics for Caregiving with Stakeholders-in-the-loop](https://mlsanity.com/paper/2210.11022/) (2022)
- [RABBIT: A Robot-Assisted Bed Bathing System with Multimodal Perception and Integrated Compliance](https://mlsanity.com/paper/2401.15159/) (2024)
- [An Adaptable, Safe, and Portable Robot-Assisted Feeding System](https://mlsanity.com/paper/2403.04134/) (2024)
- [Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms](https://mlsanity.com/paper/2311.10653/) (2023)

## Built on by (most cited first, 4 of 4)

- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [Coloring Between the Lines: Personalization in the Null Space of Planning Constraints](https://mlsanity.com/paper/2505.15503/) (2025)
- [GenPHRI: Agentic Generative Simulation for Physical Human-Robot Interaction](https://mlsanity.com/paper/2604.08664/) (2026)
- [Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination](https://mlsanity.com/paper/2610.02170/) (2026)

## Abstract (arXiv)

Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), which can vary significantly between individuals. In this work, we learn to predict personalized fROM as a way to generalize robot decision-making in a wide range of caregiving tasks. We propose a novel data-driven method for predicting personalized fROM using functional assessment scores from occupational therapy. We develop a neural model that learns to embed functional assessment scores into a latent representation of the user's physical function. The model is trained using motion capture data collected from users with emulated mobility limitations. After training, the model predicts personalized fROM for new users without motion capture. Through simulated experiments and a real-robot user study, we show that the personalized fROM predictions from our model enable the robot to provide personalized and effective assistance while improving the user's agency in action. See our website for more visualizations: https://emprise.cs.cornell.edu/grace/.