Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data

ArXi:2605.16043v1 Announce Type: cross Deformable Linear Objects (DLOs) such as ropes and cables are widely encountered in both household and industrial applications, yet remain challenging to manipulate due to their infinite-dimensional configuration space and frequent self-occlusion. Imitation learning from teleoperation offers a practical path to bimanual DLO manipulation, but its scalability is limited by human effort, making the choice of observation space critical for generalization from small datasets.