Developer creates MicroSafe-RL, an ultra-low-latency safety system that prevents RL agents from damaging expensive hardware during sim-to-real transitions.

Achieves 1.18µs latency (85 cycles on STM32 @ 72MHz) with just 20 bytes of RAM, enabling real-time safety checks on edge devices

Solves the 'Hardware Drift' problem where reinforcement learning agents encounter unknown states and destroy expensive equipment when deployed on real hardware

Model-free approach adapts to mechanical wear using EMA/MAD statistics, eliminating need for pre-trained safety models

Includes Python Auto-Tuner that generates C++ parameters from just 2 minutes of telemetry data for quick deployment