Paper recorded by Signals 4 on 2026-11-03 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04
Category: cs.LG · 机器学习 · first seen 2026-11-04
Imitation learning policies in robotics are trained on datasets collected through costly real-world teleoperation. Often, the data-collection process is unguided, relying on the teleoperator's intuition, resulting in data that contributes little to overall policy performance. Recent work has shown that curating such unguided datasets can substantially improve policy performance, but these approach