用闭环迭代优化长期机器人规划，自动修正错误并提升执行效率。
Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling
- 通过自修正机制不断优化计划直到达到平衡状态
- 在VirtualHome-Env上实现更强的长时序规划性能
- 无需额外奖励模型，可端到端训练，适合复杂任务规划
为解决自主机器人在执行任务时从高层描述生成长期动作序列的挑战，本文提出一种基于平衡序列建模的闭环长期规划方法。该方法采用自修正机制，迭代优化初始计划直至达到稳定状态，且可从分析角度端到端优化，无需构建额外验证器或奖励模型，支持简单监督学习训练。同时设计嵌套平衡序列建模流程，实现高效的闭合回路规划，能有效融入环境反馈（或内部世界模型）。在VirtualHome-Env基准上评估显示，该方法在推理时间计算量增加时表现出更优的可扩展性与先进性能。代码已开源。
原文摘要 · Abstract (English)
In the endeavor to make autonomous robots take actions, task planning is a major challenge that requires translating high-level task descriptions to long-horizon action sequences. Despite recent advances in language model agents, they remain prone to planning errors and limited in their ability to plan ahead. To address these limitations in robotic planning, we advocate a self-refining scheme that iteratively refines a draft plan until an equilibrium is reached. Remarkably, this process can be optimized end-to-end from an analytical perspective without the need to curate additional verifiers or reward models, allowing us to train self-refining planners in a simple supervised learning fashion. Meanwhile, a nested equilibrium sequence modeling procedure is devised for efficient closed-loop planning that incorporates useful feedback from the environment (or an internal world model). Our method is evaluated on the VirtualHome-Env benchmark, showing advanced performance with improved scaling w.r.t. inference-time computation. Code is available at https://github.com/Singularity0104/equilibrium-planner.
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