让智能体根据环境状态自适应调整计划，错误时能局部重规划。
SDA-PLANNER: State-Dependency Aware Adaptive Planner for Embodied Task Planning
- 构建状态依赖图，动态指导动作修正
- 错误发生时局部重构计划，成功率提升显著
- 适合复杂交互场景中的智能体规划任务
具身任务规划要求智能体在环境中以闭环方式生成可执行动作。随着大语言模型在任务分解、规划和泛化能力上的不断提升，当前方法普遍采用基于LLM的架构。然而，现有基于LLM的规划器仍存在三方面局限：固定规划范式、缺乏动作序列约束、对错误无感知。本文提出SDA-PLANNER，实现自适应规划范式，具备状态依赖感知与错误感知机制，支持全面的具身任务规划。具体而言，SDA-PLANNER引入状态依赖图，显式建模动作前提条件与效果，指导动态修正。为应对执行错误，其采用误差自适应重规划策略，包含错误回溯、诊断与自适应动作子树生成，基于当前环境状态局部重构受影响计划部分。实验表明，SDA-PLANNER在成功率与目标完成率上持续优于基线方法，尤其在多样错误条件下表现更优。
原文摘要 · Abstract (English)
Embodied task planning requires agents to produce executable actions in a close-loop manner within the environment. With progressively improving capabilities of LLMs in task decomposition, planning, and generalization, current embodied task planning methods adopt LLM-based architecture.However, existing LLM-based planners remain limited in three aspects, i.e., fixed planning paradigms, lack of action sequence constraints, and error-agnostic. In this work, we propose SDA-PLANNER, enabling an adaptive planning paradigm, state-dependency aware and error-aware mechanisms for comprehensive embodied task planning. Specifically, SDA-PLANNER introduces a State-Dependency Graph to explicitly model action preconditions and effects, guiding the dynamic revision. To handle execution error, it employs an error-adaptive replanning strategy consisting of Error Backtrack and Diagnosis and Adaptive Action SubTree Generation, which locally reconstructs the affected portion of the plan based on the current environment state. Experiments demonstrate that SDA-PLANNER consistently outperforms baselines in success rate and goal completion, particularly under diverse error conditions.
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