ChunkAct: Action Chunking for Agentic Robotics

原文：ChunkAct: Action Chunking for Agentic Robotics

作者：Bingxin Xu, Wenrui Bao, Xinxin Liu, Jiawei Zhou, Yuzhang Shang, Emilio Ferrara

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Robotics

arXiv:2610.18874v1 (cs)

[Submitted on 13 Oct 2026]

Title:ChunkAct: Action Chunking for Agentic Robotics

Authors:Bingxin Xu, Wenrui Bao, Xinxin Liu, Jiawei Zhou, Yuzhang Shang, Emilio Ferrara

View a PDF of the paper titled ChunkAct: Action Chunking for Agentic Robotics, by Bingxin Xu and 5 other authors

View PDF

HTML (experimental)

Abstract: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.

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

Cite as:

arXiv:2610.18874 [cs.RO]

(or

arXiv:2610.18874v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.18874

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

机器人 · 人工智能 · 自然语言处理 · 计算机视觉

由「前沿雷达」于 2026-10-14 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。