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

Research Efficiency & Systems

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

Merged summary

TL;DR - rMuscle accelerates Vision-Language-Action model inference by caching reusable visual-token outputs and neuron activation patterns across repetitive robot executions. It improves responsiveness by 1.29–1.42× without reducing real-world task success rates.

  • A Context Cache reuses visual-token outputs to avoid redundant computation.
  • An Action Cache reuses activation patterns to reduce model-weight accesses.
  • Online recomputation, sliding-window retrieval, and shared masks limit cache memory and access overhead.
  • Evaluations span RTX 4090 and Jetson Thor hardware, LIBERO, RoboTwin, and physical manipulation tasks.

Sources (1)

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

arXiv cs.RO Kaijun Zhou, Zhiyang Li, Le Chen, Jinyu Gu 2026-09-16 arXiv:2609.19104
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Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:15:38.525515 UTC

TL;DR - rMuscle accelerates Vision-Language-Action model inference by caching reusable visual-token outputs and neuron activation patterns across repetitive robot executions. It improves responsiveness by 1.29–1.42× without reducing real-world task success rates.

  • A Context Cache reuses visual-token outputs to avoid redundant computation.
  • An Action Cache reuses activation patterns to reduce model-weight accesses.
  • Online recomputation, sliding-window retrieval, and shared masks limit cache memory and access overhead.
  • Evaluations span RTX 4090 and Jetson Thor hardware, LIBERO, RoboTwin, and physical manipulation tasks.
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