Large Language Models (LLMs) present a promising frontier in robotic task planning by leveraging extensive human knowledge. Nevertheless, the current literature often overlooks the critical aspects of adaptability and error correction within robotic systems. This work aims to overcome this limitation by enabling robots to modify their motion strategies and select the most suitable task plans based on the context. We introduce a novel framework termed action contextualization, aimed at tailoring robot actions to the precise requirements of specific tasks, thereby enhancing adaptability through applying LLM-derived contextual insights. Our proposed motion metrics guarantee the feasibility and efficiency of adjusted motions, which evaluate robot performance and eliminate planning redundancies. Moreover, our framework supports online feedback between the robot and the LLM, enabling immediate modifications to the task plans and corrections of errors. Our framework has achieved an overall success rate of 81.25% through extensive validation. Finally, integrated with dynamic system (DS)-based robot controllers, the robotic arm-hand system demonstrates its proficiency in autonomously executing LLM-generated motion plans for sequential table-clearing tasks, rectifying errors without human intervention, and completing tasks, showcasing robustness against external disturbances. Our proposed framework features the potential to be integrated with modular control approaches, significantly enhancing robots' adaptability and autonomy in sequential task execution.

翻译：大语言模型（LLMs）通过利用广泛的人类知识，为机器人任务规划展现了广阔前景。然而，当前文献常忽视机器人系统中适应性与错误修正的关键问题。本研究通过使机器人能够根据情境调整运动策略并选择最合适的任务计划，旨在突破这一局限。我们提出了一种名为"动作情境化"的新框架，旨在根据特定任务的具体需求定制机器人动作，从而通过应用LLM生成的上下文洞察增强适应性。我们提出的运动度量指标可评估机器人性能并消除规划冗余，确保调整后运动的可行性与效率。此外，本框架支持机器人与LLM之间的在线反馈，能够即时修改任务计划并修正错误。通过大量验证，该框架实现了81.25%的整体成功率。最终，与基于动态系统（DS）的机器人控制器集成后，机械臂-手系统展示了其在连续桌面清理任务中自主执行LLM生成的运动规划、无需人工干预即可修正错误并完成任务的能力，同时展现出对外部干扰的鲁棒性。本框架具有与模块化控制方法集成的潜力，能显著提升机器人在连续任务执行中的适应性与自主性。