The Microduck project demonstrates a practical path for bringing reinforcement learning to affordable hardware. The author details the journey from training on NVIDIA GPUs to deploying on an RK3566 system-on-chip, a common choice for edge AI due to its balance of performance and cost. Key challenges include model size reduction, inference latency, and power management, all critical for real-time robotic control. The post provides concrete examples of quantization and pruning techniques that made the deployment feasible. For developers and hobbyists, this serves as a blueprint for similar edge-AI robotics projects, highlighting that advanced AI is no longer confined to high-end servers. The hands-on nature of the write-up adds credibility and practical value, making it a must-read for those exploring embedded machine learning.
A hands-on account of moving a reinforcement learning robot from GPU training to a low-cost RK3566 embedded board, covering optimization and real-world deployment challenges.