vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

Title:vla.simd: Efficient CPU Inference for Language-Conditioned Manipulation

Abstract:Deploying language-conditioned manipulation without a dedicated GPU requires efficient inference and action chunks that cover the delay between policy queries. We present this http URL, a CPU inference engine that combines shared SIMD micro-kernels, reusable computation, and target-specific optimization. We relate query latency and execution horizon to action availability under lagged and time-aligned execution, distinguishing action supply from feedback frequency. Across six policies and four CPUs, this http URL achieves approximately $1.4\times$ median speedup over compiled PyTorch references while preserving fp32 numerical fidelity. We also introduce IMPACT, an ACT-based policy with cached text representations and language-modulated visual features. IMPACT is the only language-conditioned policy in our evaluated set that supplies at least 30 actions/s on the Raspberry Pi 5: after a 90 s thermal soak, it supplies 33.5 actions/s in fp32 and 81.2 with int8. Separate GPU evaluations yield $76.4\%$ mean success across four LIBERO suites without robot pretraining; instruction-shuffling tests demonstrate selection among familiar goals. Trials with IMPACT on an SO-101 arm and SmolVLA on a UR10e with a Robotiq gripper demonstrate CPU deployment on two robot embodiments.

8 pages, 7 tables, 5 figures. Project page: this https URL

Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)

arXiv:2609.24274 [cs.RO]

(or arXiv:2609.24274v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.24274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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