# Offline Imitation Learning

Offline imitation learning usually demands demonstrations that are both safe and high-performing, or a hand-written cost function, and neither is easy to obtain in real settings. DexDICE separates the data into safe demonstrations and performant demonstrations, uses the safe set to define a safe support region in which the policy may act, and then matches the performant behavior's distribution inside that region, training without reward or cost labels. On 38 re-curated DSRL tasks it keeps group-averaged cost below the constraint and attains the highest mean return among safe methods; on a SUMMIT-XL mobile robot trained from 20 human teleoperation episodes, it reached the goal in all 10 trials with zero safety violations.

Offline imitation learning usually demands demonstrations that are both safe and high-performing, or a hand-written cost function, and neither is easy to obtain in real settings. DexDICE separates the data into safe demonstrations and performant demonstrations, uses the safe set to define a safe support region in which the policy may act, and then matches the performant behavior's distribution inside that region, training without reward or cost labels. On 38 re-curated DSRL tasks it keeps group-averaged cost below the constraint and attains the highest mean return among safe methods; on a SUMMIT-XL mobile robot trained from 20 human teleoperation episodes, it reached the goal in all 10 trials with zero safety violations.