# Motion Retargeting

OTRetarget jointly retargets robot and multi-object motion by representing surface interactions through signed distances, closest surface points and relative directions, transferring these quantities across human, robot and object geometries with entropic optimal transport, and enforcing them in a per-frame constrained inverse kinematics that optimizes robot and object poses jointly; on OMOMO it reaches a robot-object interaction Jaccard of 87% and a depth error of 8.7 mm versus 28% and 29.3 mm for OmniRetarget, and transfers to a physical Unitree G1 for two-handed box pick-and-place onto a table.

OTRetarget jointly retargets robot and multi-object motion by representing surface interactions through signed distances, closest surface points and relative directions, transferring these quantities across human, robot and object geometries with entropic optimal transport, and enforcing them in a per-frame constrained inverse kinematics that optimizes robot and object poses jointly; on OMOMO it reaches a robot-object interaction Jaccard of 87% and a depth error of 8.7 mm versus 28% and 29.3 mm for OmniRetarget, and transfers to a physical Unitree G1 for two-handed box pick-and-place onto a table.

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.

HuRo introduces a robotization pipeline that converts heterogeneous egocentric human videos into robot-aligned observations and action trajectories, builds the HuRo dataset of about 142 million frames from five sources (EgoDex, EgoVerse, Ego4D, Ego10K, and EPIC-Kitchens), and uses it to pretrain VLA policies on the ALLEX bimanual dexterous robot: as robotized data increases, average completion across four real-world manipulation tasks rises from 51.5% without pretraining to 80.3% with the full dataset, with ID completion from 68.1% to 88.4% and OOD completion from 34.9% to 72.2%.

HuRo introduces a robotization pipeline that converts heterogeneous egocentric human videos into robot-aligned observations and action trajectories, builds the HuRo dataset of about 142 million frames from five sources (EgoDex, EgoVerse, Ego4D, Ego10K, and EPIC-Kitchens), and uses it to pretrain VLA policies on the ALLEX bimanual dexterous robot: as robotized data increases, average completion across four real-world manipulation tasks rises from 51.5% without pretraining to 80.3% with the full dataset, with ID completion from 68.1% to 88.4% and OOD completion from 34.9% to 72.2%.