Newest papers

Fast Motion Retargeting Pipeline for Scalable Humanoid Robot Learning

Daniel Marew, Sangjun Park, Nisal Perera, Donghyun Kim · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28470 · cited by 0 works (OpenAlex) · open-access PDF

German

Während große menschliche Bewegungsempfangsdatensätze eine reiche Quelle für Demonstrationen für das humanoide Roboterlernen bieten, bleibt das Retargeting der vielfältigen Bewegungen auf verschiedene humanoide Roboter eine nicht-triviale Herausforderung. Die bestehenden kinodynamischen Trajectory-Optimierungspipelines sind rechnerisch teuer und erfordern oft manuelle Anpassungen pro Bewegung. Wir präsentieren FRDV (Fast Retargeting with Dynamics Validation), ein skalierbares Rahmenwerk, das aus schnellem begrenztem kinematischem Retargeting und einer zusätzlichen Dynamik-Validierungspipeline besteht. Seine kinematische Phase integriert einen maßgeschneiderten Sequential Quadratic Programming Solver, Echtzeit-Iteration und

Machine translation (Meta M2M100); the English original follows.

Abstract

While large-scale human motion capture datasets provide a rich source of demonstrations for humanoid robot learning, retargeting the diverse motions to different humanoid robots remains a nontrivial challenge. Existing kinodynamic trajectory optimization pipelines are computationally expensive and often require manual adjustments per motion. We present FRDV (Fast Retargeting with Dynamics Validation), a scalable framework consisting of rapid constrained kinematic retargeting and an additional dynamics validation pipeline. Its kinematic stage integrates a custom Sequential Quadratic Programming solver, Real-Time Iteration, and parallel chunk-based processing, achieving up to 9,156 frames per second, approximately up to 136 times faster than GMR and ProtoMotions, while robot-specific constraints are incorporated into the optimization to reduce common retargeting artifacts, such as ground penetration and self-collision. For motions requiring additional verification, a contact implicit iLQR controller is utilized to test the retargeted reference under full rigid body dynamics. This dynamics-based validation identifies dynamically infeasible motions and refines contact-related artifacts such as foot floating and foot skating. We evaluate retargeting quality by training motion tracking reinforcement learning policies using several state-of-the-art pipelines and comparing their tracking fidelity and success rates. Furthermore, sim-to-real transfer is demonstrated on a Unitree G1 humanoid. Videos and code are available at: https://daroslab.github.io/frdv/.

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Topics: Retargeting, Computer science, Artificial intelligence, Computer vision, Humanoid robot, Kinematics

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