RoboInspector：揭示用于基于大语言模型的机器人操作的策略代码的不可靠性
RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulation
AI总结：
研究基于大语言模型的机器人操作中策略代码不可靠问题，设计RoboInspector从任务复杂性和指令粒度揭示其不可靠性，通过实验识别四种不可靠行为及原因，还引入优化方法，可提高策略代码生成可靠性达35%。
摘要：
大语言模型（LLMs）在推理和代码生成方面展现出卓越能力，能让机器人操作仅通过一条指令启动，其通过生成控制机器人所需的策略代码来执行各种任务。尽管LLMs有进展，但由于现实世界任务的多样需求和用户指令的内在复杂性，实现可靠的策略代码生成仍是重大挑战。不同用户对同一任务可能给出不同指令，导致策略代码生成不可靠。为弥合这一差距，我们设计了RoboInspector，从操作任务复杂性和指令粒度两个角度揭示并刻画基于LLM的机器人操作中策略代码的不可靠性。我们在两个突出框架中对216种不同的任务、指令和LLMs组合进行了全面实验。RoboInspector识别出导致操作失败的四种主要不可靠行为，并详细刻画了这些行为及其潜在原因，为实际开发提供了减少不可靠性的见解。此外，我们引入了一种由失败策略代码反馈引导的优化方法，在模拟和现实环境中评估，该方法在基于LLM的机器人操作中可将策略代码生成的可靠性提高多达35%。
英文摘要：
Large language models (LLMs) demonstrate remarkable capabilities in reasoning and code generation, enabling robotic manipulation to be initiated with just a single instruction. The LLM carries out various tasks by generating policy code required to control the robot. Despite advances in LLMs, achieving reliable policy code generation remains a significant challenge due to the diverse requirements of real-world tasks and the inherent complexity of user instructions. In practice, different users may provide distinct instructions to drive the robot for the same task, which may cause the unreliability of policy code generation. To bridge this gap, we design \textbf{RoboInspector}, a pipeline to unveil and characterize the unreliability of the policy code for LLM-enabled robotic manipulation from two perspectives: the complexity of the manipulation task and the granularity of the instruction. We perform comprehensive experiments with 216 distinct combinations of tasks, instructions, and LLMs in two prominent frameworks. The \textbf{RoboInspector} identifies four main unreliable behaviors that lead to manipulation failure. We provide a detailed characterization of these behaviors and their underlying causes, giving insight for practical development to reduce unreliability. Furthermore, we introduce a refinement approach guided by failure policy code feedback that improves the reliability of policy code generation by up to 35\% in LLM-enabled robotic manipulation, evaluated in both simulation and real-world environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
