2026
Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors Through Physics-Aware Neural Architectures
RA-L 2026
Efficiently training control policies for robots is a major challenge that can greatly benefit from utilizing knowledge gained from training similar systems through cross-embodiment knowledge transfer. In this work, we focus on accelerating policy training using a library-based initialization scheme