ADM-Planner：基于注意力增强动态记忆的LLM引导移动机械臂长时程规划
ADM-Planner: LLM-Guided Long-Horizon Planning for Mobile Manipulators with Attention-Enhanced Dynamic Memory
- Nanyang Technological University（南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对移动机械臂长时程规划中场景动态变化导致计划失效的问题，提出ADM-Planner框架，通过注意力增强动态记忆分离持久知识与对象状态，有界检索关键信息，在模拟和实物实验中显著提升成功率并降低上下文开销。
中文摘要 AI 辅助
大型语言模型能够将移动机械臂的目标分解为长的动作序列，但由此产生的计划仅在当前世界上下文有效时保持可靠。固定的场景描述在物体被发现、移动或完成时变得过时，而保留所有观察结果则会产生包含冗余和冲突状态的不断增长的历史记录。为解决这一矛盾，我们提出了一种LLM引导的规划框架ADM-Planner，其具有注意力增强的动态记忆（ADM）。持久的工作空间知识与以对象为中心的状态分离，异步观察和动作结果更新该状态，一个有界的检索器仅暴露可能影响下一个决策的条目。当更新使剩余计划失效时，LLM会重新规划。在1,500个任务模拟器回合中，所提出的ADM在14个容器的嘈杂动态设置中实现了100%的全任务成功率，而静态记忆为62%，未过滤的动态记忆为97%，同时相对于后者将上下文大小代理减少了95.8%。在六回合的实时GPT-5 Mini规划器中，两种动态记忆变体都完成了每个任务，而ADM将提供商报告的输入令牌减少了14.4%，平均规划器调用次数从7.0次降至6.0次。一项单独的60次试验的PyBullet研究保留了ADM的100%成功率，而静态记忆为50%。最后，配备ADM-Planner的移动机械臂在室内和室外物理实验中完成了各种任务，同时整合了执行开始后才揭示的目标。结果表明，使用ADM进行选择性状态维护，而非仅依赖提示历史，是在变化环境中进行长时程规划的实用基础。项目页面：此https URL。
英文摘要
Large language models can decompose mobile-manipulation goals into long action sequences, but the resulting plans remain reliable only while their world context is current. A fixed scene description becomes stale when objects are discovered, moved, or completed while retaining every observation instead produces a growing history with redundant and conflicting state. To resolve this tension, we present an LLM-guided planning framework ADM-Planner with attention-enhanced dynamic memory (ADM). Persistent workspace knowledge is separated from object-centric state, asynchronous observations and action outcomes update that state, and a bounded retriever exposes only the entries that can affect the next decision. The LLM replans when an update invalidates the remaining plan. Across 1,500 task-simulator episodes, the proposed ADM achieved 100% full-task success in the 14-container noisy dynamic setting, compared with 62% for static memory and 97% for unfiltered dynamic memory, while reducing the context-size proxy by 95.8% relative to the latter. In a six-episode live GPT-5 Mini planner, both dynamic memory variants completed every mission, while ADM reduced provider-reported input tokens by 14.4% and mean planner calls from 7.0 to 6.0. A separate 60-trial PyBullet study retained 100% success for ADM, compared with 50% for static memory. Finally, the mobile manipulator with ADM-Planner completed various missions in indoor and outdoor physical experiments while incorporating targets revealed after execution began. The results show that selective state maintenance with ADM, rather than prompt history alone, is a practical basis for long-horizon planning in changing environments. Project page: https://xjp99v5.github.io/ADM-Planner
