2 papers
cs.RO2025
Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions from Expert Demonstrations
Baiyu Peng, Aude Billard
Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to…
cs.LG2025
Learning Constraint Network from Demonstrations via Positive-Unlabeled Learning with Memory Replay
Baiyu Peng, Aude Billard
Planning for a wide range of real-world tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to…