具有物理约束的原位价值对齐人机交互 (In-situ Value-aligned Human-Robot Interactions with Physical Constraints)

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 .

翻译：配备大型语言模型（LLMs）后，以人为中心的机器人现已能够执行一系列先前被认为具有挑战性或难以实现的任务。然而，对于认知机器人而言，仅完成任务是不够的，它们还应学习并应用人类偏好至未来场景。本研究提出一个将人类偏好与物理约束相结合的框架，要求机器人在完成任务时同时兼顾二者。首先，我们开发了一个日常家庭活动基准，这些活动通常基于特定偏好进行评估。随后，我们引入了基于人类反馈的上下文学习（ICLHF），其中人类反馈来源于直接指令以及日常生活中有意或无意的调整。通过大量实验测试ICLHF生成任务计划并平衡物理约束与偏好的能力，结果证明了我们方法的有效性。项目页面：https://iclhf.github.io。