Tara Sadjadpour

The work presents Morphometric Imitation, a three-stage framework that first kinematically retargets human hand-object interaction to robot hands of different morphologies while preserving demonstrated contacts via morphometric optimization (MMO), then uses residual reinforcement learning with object pose and contact information to produce dynamically feasible demonstrations, and finally distills them into visuomotor policies; across three robot hands and ten GRAB trajectories, MMO improves contact F1 over the strongest of five baselines by at least 8 points, downstream dynamic retargeting success by as much as 35 points, and the distilled policies achieve 89.3% zero-shot success in 300 real-world trials on 30 objects.

The work presents Morphometric Imitation, a three-stage framework that first kinematically retargets human hand-object interaction to robot hands of different morphologies while preserving demonstrated contacts via morphometric optimization (MMO), then uses residual reinforcement learning with object pose and contact information to produce dynamically feasible demonstrations, and finally distills them into visuomotor policies; across three robot hands and ten GRAB trajectories, MMO improves contact F1 over the strongest of five baselines by at least 8 points, downstream dynamic retargeting success by as much as 35 points, and the distilled policies achieve 89.3% zero-shot success in 300 real-world trials on 30 objects.