迈向可靠的代码即策略：一种用于具身任务规划的神经符号框架
Towards Reliable Code-as-Policies: A Neuro-Symbolic Framework for Embodied Task Planning
AI总结：
提出神经符号框架，在代码生成中集成符号验证与交互验证，提升动态部分可观测环境下的具身任务规划可靠性，成功率较基线提高46.2%。
摘要：
近年来，大型语言模型（LLM）的进展使得能够为具身智能体（如机器人）自动生成用于任务规划和控制的可执行代码，展示了基于LLM的具身智能的潜力。然而，这些基于LLM的代码即策略方法常常受限于环境接地不足，特别是在动态或部分可观测的环境中，导致由于代码生成错误或不完整而任务成功率欠佳。在本工作中，我们提出了一种神经符号具身任务规划框架，该框架在代码生成过程中引入了显式的符号验证和交互式验证过程。在验证阶段，框架生成探索性代码，主动与环境交互以获取缺失的观测，同时保留任务相关的状态。这一集成过程增强了生成代码的接地性，从而在复杂环境中提高了任务可靠性和成功率。我们在RLBench及真实世界环境中，跨动态、部分可观测场景评估了我们的框架。实验结果表明，我们的框架相比代码即策略基线将任务成功率提高了46.2%，并实现了超过86.8%的任务相关动作可执行性，从而增强了动态环境中任务规划的可靠性。
英文摘要：
Recent advances in large language models (LLMs) have enabled the automatic generation of executable code for task planning and control in embodied agents such as robots, demonstrating the potential of LLM-based embodied intelligence. However, these LLM-based code-as-policies approaches often suffer from limited environmental grounding, particularly in dynamic or partially observable settings, leading to suboptimal task success rates due to incorrect or incomplete code generation. In this work, we propose a neuro-symbolic embodied task planning framework that incorporates explicit symbolic verification and interactive validation processes during code generation. In the validation phase, the framework generates exploratory code that actively interacts with the environment to acquire missing observations while preserving task-relevant states. This integrated process enhances the grounding of generated code, resulting in improved task reliability and success rates in complex environments. We evaluate our framework on RLBench and in real-world settings across dynamic, partially observable scenarios. Experimental results demonstrate that our framework improves task success rates by 46.2% over Code-as-Policies baselines and attains over 86.8% executability of task-relevant actions, thereby enhancing the reliability of task planning in dynamic 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。
