Fleet Learning

Fleet learning focuses on enabling groups of robots to collaboratively learn and improve their performance from decentralized, heterogeneous data streams, overcoming limitations of centralized approaches. Current research emphasizes efficient policy merging techniques, often employing recurrent neural networks, and interactive learning methods that leverage human supervision, incorporating uncertainty quantification to improve robustness. This field is significant for advancing robotic autonomy in complex tasks, particularly in scenarios with limited communication bandwidth or data storage, and has implications for diverse applications including manufacturing, logistics, and renewable energy.