Returaj Burnwal

SafeMIL: Learning Offline Safe Imitation Policy from Non-Preferred Trajectories

In this work, we study the problem of offline safe imitation learning (IL). In many real-world settings, online interactions can be risky, and accurately specifying the reward and the safety cost information at each timestep can be difficult. However, it is often feasible to collect trajectories ref