Learning to Optimize Control Policies and Evaluate Reproduction Performance from Human Demonstrations
Paul Gesel,Dain LaRoche,Sajay Arthanat,Momotaz Begum,Paul Gesel,Dain LaRoche,Sajay Arthanat,Momotaz Begum
We are interested in learning from demonstration (LfD) that can both learn and execute a trajectory and evaluate the quality of a previously unseen trajectory in the domain of assistive robotics. To this end, we propose a novel continuous inverse optimal control (IOC) formulation that simultaneously learns an optimal time-invariant controller and an evaluation metric from human demonstrations. We ...
