Robust Real-time Whole-Body Motion Retargeting from Human to Humanoid

Luigi Penco (1) , Brice Clément (1) , Valerio Modugno (2) , Enrico Mingo Hoffman (3) , Gabriele Nava (3) , Daniele Pucci (3) , Nikos Tsagarakis (4) , Jean-Baptiste Mouret (1) , Serena Ivaldi (1)

1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
2 DIAG UNIROMA - Dipartimento di Ingegneria informatica automatica e gestionale [Roma]
3 IIT - Italian Institute of Technology = Istituto Italiano di Tecnologia
4 IIT - Istituto Italiano di Tecnologia

Transferring the motion from a human operator to a humanoid robot is a crucial step to enable robots to learn from and replicate human movements. The ability to retarget in real-time whole-body motions that are challenging for the humanoid balance is critical to enable human to humanoid teleoperation. In this work, we design a retargeting framework that allows the robot to replicate the motion of the human operator, acquired by a wearable motion capture suit, while maintaining the whole-body balance. We introduce some dynamic filter in the retargeting to forbid dangerous motions that can make the robot fall. We validate our approach through several experiments on the iCub robot, which has a significantly different body structure and size from the one of the human operator.

Domaines: Robotique [cs.RO]; Automatique / Robotique

Luigi Penco, Brice Clément, Valerio Modugno, Enrico Mingo Hoffman, Gabriele Nava, et al.. Robust Real-time Whole-Body Motion Retargeting from Human to Humanoid. HUMANOIDS 2018 - IEEE-RAS 18th International Conference on Humanoid Robots, Nov 2018, Beijing, China. ⟨hal-01895145⟩