Micro Neural Policies for Safe Real-Time Robotic Control

Abstract:In this paper, we investigate the synthesis of Micro Neural Policies (MNP) to enable safe and robust real-time robotic control on computationally constrained embedded devices. We demonstrate that integrating Evolution Strategy (ES) and Statistical Model Checking (SMC)-based verification for policy search can drastically reduce neural network size without compromising safety and robustness. We conduct a large-scale training and evaluation of MNP on Cartpole and Quadrotor control tasks, varying control frequencies and network architectures. After validating these policies in simulation, we evaluate their deployability through zero-shot transfer to physical systems. Our experiments show that MNP can successfully achieve safe sim-to-real transfer without sacrificing control performance. We then show that the policies' memory footprint, ranging from 0.5 to 7.5 kB, allows deployment on microcontrollers, where they achieve real-time inference latency with under 25 ns of jitter while leaving the chip idle for over 97% of the time for additional workloads. This makes them a highly practical solution for severely resource-constrained robotic systems.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.08541 [cs.RO]

(or arXiv:2610.08541v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.08541 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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