Micro Neural Policies for Safe Real-Time Robotic Control

Key points

• 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.1
• We conduct a large-scale training and evaluation of MNP on Cartpole and Quadrotor control tasks, varying control frequencies and network architectures.1
• Our experiments show that MNP can successfully achieve safe sim-to-real transfer without sacrificing control performance.1
• 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.1

Sources (1)

• [1]Micro Neural Policies for Safe Real-Time Robotic Control
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:29 PM

  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.

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