Van Duong

DROID: Learning from Offline Heterogeneous Demonstrations via Reward-Policy Distillation

Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-error learning is infeasible or specifying a cost function is difficult, such as robotic surgery, autonomous driving, and path-finding for NASA's Mars rovers. However, two key problems remain challenging in OLfD: 1) h…