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2026-09-17rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference
Kaijun Zhou, Zhiyang Li, Le Chen, Jinyu Gu
arXiv:2609.19104v1Robots that use vision-language-action models are getting better at repetitive factory tasks, but they still feel sluggish because every new step forces the model to recompute a lot of the same information. This paper asks a practical question: can we make robot inference faster by exploiting the fact that many industrial tasks are highly repetitive? The core idea is a kind of robotic muscle memory. The system, called rMuscle, caches two things across repeated executions: visual-token computations from the scene, and internal action-related activations from the model itself. It then reuses those cached results with lightweight online updates, so the robot spends less time thinking and more time acting. On real and simulated manipulation benchmarks, rMuscle speeds inference by up to 1.42 times while keeping task success rates unchanged. That matters because faster inference can mean smoother motion, lower latency, and more reliable robots in the real world.
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