Action Contextualization: Adaptive Task Planning and Action Tuning using Large Language Models
arxiv(2024)
摘要
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
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.
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