Learning Human-Aware Robot Policies for Adaptive Assistance

Jason Qin, Shikun Ban, Wentao Zhu, Yizhou Wang, Dimitris Samaras

Abstract: Developing robots that can assist humans efficiently, safely, and adaptively is crucial for real-world applications such as healthcare. While previous work often assumes a centralized simulation system for co-optimizing human-robot interactions, we argue that real-world scenarios are much more complicated, as humans have individual preferences regarding how tasks are performed. Robots typically lack direct access to these implicit preferences. However, to provide effective assistance, robots must still be able to recognize and adapt to the individual needs and preferences of different users. To address these challenges, we first delineate the misalignment problem in human and robot observations, then propose a novel framework in which robots infer human intentions and reason about human utilities through interaction. Our approach features two critical modules: the anticipation module is a motion predictor that captures the spatial-temporal relationship between the robot agent and user agent, which contributes to predicting human behavior; the utility module infers the underlying human utility functions through progressive task demonstration sampling. Extensive experiments across various robot types and assistive tasks demonstrate that the proposed framework not only enhances task success but also significantly improves human preference alignment in simulated environments, paving the way for more personalized and adaptive assistive robotic systems.

External IDs:dblp:journals/ral/QinBZWS25