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ChunkAct: Action Chunking for Agentic Robotics

Paper recorded by Signals 4 on 2026-10-13 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-13 on arXiv · recorded by Signals 4 on 2026-10-14

Category: cs.AI · 人工智能 · first seen 2026-10-14

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 cos

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#321 most recent of 960 cs.AI papers we have recorded · ↑ newer: Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group S · ↓ older: What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empiric
Cite this page: ChunkAct: Action Chunking for Agentic Robotics: the #321 most recent of 960 cs.AI papers we have recorded (as of 2026-10-13). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/chunkact-action-chunking-for-agentic-robotics.html
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