kiwi-sherbet/TRILL

Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation

Mingyo Seo, Steve Han, Kyutae Sim, Seung Hyeon Bang, Carlos Gonzalez, Luis Sentis, Yuke Zhu

Project | arXiv | code

Abstract

We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The challenge of collecting human demonstrations for humanoids, in conjunction with the difficulty of policy training under a high degree of freedom, presents substantial challenges. We introduce TRILL, a data-efficient framework for learning humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands from human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid robots, our method can efficiently learn complex loco-manipulation skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various types of tasks.

Citing

@inproceedings{seo2023trill,
   title={Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation},
   author={Seo, Mingyo and Han, Steve and Sim, Kyutae and 
           Bang, Seung Hyeon and Gonzalez, Carlos and 
           Sentis, Luis and Zhu, Yuke},
   booktitle={IEEE-RAS International Conference on Humanoid Robots (Humanoids)},
   year={2023}
}