3 papers
cs.LG2026
OSIL: Learning Offline Safe Imitation Policies with Safety Inferred from Non-preferred Trajectories
Returaj Burnwal, Nirav Pravinbhai Bhatt, Balaraman Ravindran
This work addresses the problem of offline safe imitation learning (IL), where the goal is to learn safe and reward-maximizing policies from demonstrations that do not have per-tim…
cs.LG2025
SafeMIL: Learning Offline Safe Imitation Policy from Non-Preferred Trajectories
Returaj Burnwal, Nirav Pravinbhai Bhatt, Balaraman Ravindran
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…
cs.LG2025
Learning from Observation: A Survey of Recent Advances
Returaj Burnwal, Hriday Mehta, Nirav Pravinbhai Bhatt +1
Imitation Learning (IL) algorithms offer an efficient way to train an agent by mimicking an expert's behavior without requiring a reward function. IL algorithms often necessitate a…