In-situ Value-aligned Human-Robot Interactions with Physical Constraints
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914196399063040 |
|---|---|
| author | Li, Hongtao Jiao, Ziyuan Liu, Xiaofeng Liu, Hangxin Zheng, Zilong |
| author_facet | Li, Hongtao Jiao, Ziyuan Liu, Xiaofeng Liu, Hangxin Zheng, Zilong |
| contents | Equipped with Large Language Models (LLMs), human-centered robots are now capable of performing a wide range of tasks that were previously deemed challenging or unattainable. However, merely completing tasks is insufficient for cognitive robots, who should learn and apply human preferences to future scenarios. In this work, we propose a framework that combines human preferences with physical constraints, requiring robots to complete tasks while considering both. Firstly, we developed a benchmark of everyday household activities, which are often evaluated based on specific preferences. We then introduced In-Context Learning from Human Feedback (ICLHF), where human feedback comes from direct instructions and adjustments made intentionally or unintentionally in daily life. Extensive sets of experiments, testing the ICLHF to generate task plans and balance physical constraints with preferences, have demonstrated the efficiency of our approach. Project page: https://iclhf.github.io . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07606 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-situ Value-aligned Human-Robot Interactions with Physical Constraints Li, Hongtao Jiao, Ziyuan Liu, Xiaofeng Liu, Hangxin Zheng, Zilong Robotics Equipped with Large Language Models (LLMs), human-centered robots are now capable of performing a wide range of tasks that were previously deemed challenging or unattainable. However, merely completing tasks is insufficient for cognitive robots, who should learn and apply human preferences to future scenarios. In this work, we propose a framework that combines human preferences with physical constraints, requiring robots to complete tasks while considering both. Firstly, we developed a benchmark of everyday household activities, which are often evaluated based on specific preferences. We then introduced In-Context Learning from Human Feedback (ICLHF), where human feedback comes from direct instructions and adjustments made intentionally or unintentionally in daily life. Extensive sets of experiments, testing the ICLHF to generate task plans and balance physical constraints with preferences, have demonstrated the efficiency of our approach. Project page: https://iclhf.github.io . |
| title | In-situ Value-aligned Human-Robot Interactions with Physical Constraints |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.07606 |