Best Conference Paper Award and the Best Paper Award on Robot Manipulation and Locomotion at #ICRA2026!
High-quality motion reference data is key for humanoid skill learning 🤖🕺💃
A natural idea is to leverage human motions and “translate” them to humanoid motions, a process known as retargeting. For interaction-rich tasks such as scene interaction and loco-manipulation, retargeting is challenging: it must ensure motion consistency, smoothness, kinematic feasibility (no artifacts like penetration or foot skating), and scalability (one framework can handle thousands of motions).
Excited to release OmniRetarget — a scalable retargeting method with a 4-hour high-quality humanoid motion dataset for interaction-rich tasks. OmniRetarget takes an interaction-preserving perspective: we optimize Laplacian deformation between source and target interaction meshes while enforcing kinematic constraints, producing consistent, smooth, and feasible trajectories at scale. Even better, OmniRetarget can efficiently augment motions by varying terrains, objects, and initial poses.
This high-quality interaction-preserving retargeting enables a minimal RL setup to execute long-horizon (up to 30s) agile, interaction-rich skills. All tasks in the video share just 5 rewards, 4 domain randomization terms, and rely only on proprioception.
More details: https://omniretarget.github.io/
