Quality over Quantity: Demonstration Curation via Influence Functions for Data-Centric Robot Learning

ArXi:2603.09056v1 Announce Type: cross Learning from nstrations has emerged as a promising paradigm for end-to-end robot control, particularly when scaled to diverse and large datasets. However, the quality of nstration data, often collected through human teleoperation, remains a critical bottleneck for effective data-driven robot learning. Human errors, operational constraints, and teleoperator variability