ChunkAct：面向智能体机器人学的动作分块技术
ChunkAct: Action Chunking for Agentic Robotics
AI总结：
ChunkAct将动作分块引入智能体机器人学，通过编译记忆为可自主执行的动作分块，在LIBERO-Pro上提升任务成功率并降低LLM计算开销。
摘要：
智能体机器人学将大型语言模型（LLM）智能体置于机器人控制循环中，该智能体在记忆的引导下整合感知、运动及视觉-语言-动作（VLA）工具，从而无需重新训练即可泛化至新任务。但这种泛化性的代价是效率极低：每个基础动作都需要对完整交互历史进行一轮LLM推理，因此一个任务回合的成本随基础动作数量增加而上升。我们发现这类推理存在大量冗余：一旦选定子目标，其对应的基础动作会遵循记忆中已预设的流程。本文提出ChunkAct，将动作分块引入智能体机器人学。在线阶段，单次调用即可启动一串链式基础动作组成的分块，该分块会自主执行，仅在失败时才返回控制权；离线阶段，ChunkAct会将记忆编译为这类分块，因此测试时执行无需依赖记忆，规划器仅用于需要推理的决策。如此一来，LLM计算量随决策数量而非基础动作数量扩展。在LIBERO-Pro数据集上，ChunkAct相较于智能体机器人学领域的现有最优方法，任务成功率提升了5.0个百分点，同时LLM调用次数减少2.9倍、输入token减少4.4倍、输出token减少2.3倍。
英文摘要：
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM reasoning over the full interaction history, so the cost of an episode grows with the number of primitives. We find this reasoning largely redundant: once a sub-goal is chosen, its primitives follow a routine already prescribed by memory. We present ChunkAct, which introduces action chunking to agentic robotics. Online, a single call launches a chunk of chained primitives, which executes autonomously and returns control only upon failure. Offline, ChunkAct compiles the memory into these chunks, so execution is memory-free at test time and the planner is reserved for decisions that require reasoning. LLM computation therefore scales with the number of decisions rather than the number of primitives. On LIBERO-Pro, ChunkAct improves task success by 5.0 points over the state of the art in agentic robotics while requiring 2.9x fewer LLM calls, 4.4x fewer input tokens, and 2.3x fewer output tokens.
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。
