In real-world human-robot systems, it is essential for a robot to comprehend human objectives and respond accordingly while performing an extended series of motor actions. Although human objective alignment has recently emerged as a promising paradigm in the realm of physical human-robot interaction, its application is typically confined to generating simple motions due to inherent theoretical limitations. In this work, our goal is to develop a general formulation to learn manipulation functional modules and long-term task goals simultaneously from physical human-robot interaction. We show the feasibility of our framework in enabling robots to align their behaviors with the long-term task objectives inferred from human interactions.

翻译：在真实世界的人机系统中，机器人在执行一系列连续动作时，理解人类目标并做出相应响应至关重要。尽管人类目标对齐已成为物理人机交互领域具有前景的新范式，但由于其固有的理论局限性，该方法通常仅适用于生成简单运动。本研究旨在提出一种通用框架，通过物理人机交互同时学习操作功能模块与长期任务目标。我们证明了该框架能够使机器人根据从人类交互中推断出的长期任务目标来调整自身行为。