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rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference

Paper recorded by Signals 4 on 2026-09-16 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.AI · 人工智能 · first seen 2026-09-17

Abstract

Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies. Vision-Language-Action (VLA) models now dominate as the policy paradigm for these robots. The inference latency of VLA models directly affects robot responsiveness and motion smoothness. However, ex

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#27 most recent of 300 cs.AI papers we have recorded · ↑ newer: Flag Game: A Toy Model for Mechanistic Swarm Interpretability · ↓ older: Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for
Cite this page: rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference: the #27 most recent of 300 cs.AI papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rmuscle-robotic-muscle-memory-for-efficient-vision-language-action-model-inferen.html
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