We study visual domain transfer for end-to-end imitation learning in a realistic and challenging setting where target-domain data are strictly off-policy, expert-free, and scarce. …
Guaranteeing constraint satisfaction is a challenging problem in imitation learning (IL), especially when the task involves operating at the handling limits of the system. While …
Designed a constraint-aware imitation learning framework incorporating an actor-critic structure for explicit safety modeling.
Explored a goal-conditioned, data-driven MPC pipeline that learns transition costs between feasible states from closed-loop trajectories via imitation learning.