让机器人在执行任务时兼顾人类偏好与物理限制。
In-situ Value-aligned Human-Robot Interactions with Physical Constraints
- 通过日常行为反馈学习人类偏好，实现情境化指令理解。
- 在家庭任务中同时满足物理约束与偏好，提升任务完成率。
- 适合需要安全、贴心交互的智能服务机器人研究者。
配备大型语言模型（LLMs）的人本机器人已能执行大量以往被认为困难或不可能的任务。然而，仅完成任务不足以满足认知型机器人的需求，它们还应学会并应用人类偏好以应对未来场景。本文提出一种融合人类偏好与物理约束的框架，要求机器人在执行任务时兼顾二者。首先，我们构建了一个涵盖日常家庭活动的基准数据集，这些任务常基于特定偏好进行评估。随后，我们引入上下文学习的人类反馈（ICLHF），其中人类反馈来自日常生活中的直接指令及有意或无意的行为调整。大量实验验证了该方法在生成任务计划、平衡物理约束与偏好方面的有效性。
原文摘要 · Abstract (English)
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 .
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