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--- |
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license: mit |
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language: |
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- en |
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--- |
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# Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation |
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[Mingyo Seo](https://mingyoseo.com), [Steve Han](https://www.linkedin.com/in/stevehan2001), [Kyutae Sim](https://www.linkedin.com/in/kyutae-sim-888593166), [Seung Hyeon Bang](https://sites.utexas.edu/hcrl/people/), [Carlos Gonzalez](https://sites.utexas.edu/hcrl/people/), [Luis Sentis](https://sites.google.com/view/lsentis), [Yuke Zhu](https://www.cs.utexas.edu/~yukez) |
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[Project](https://ut-austin-rpl.github.io/TRILL) | [arXiv](https://arxiv.org/abs/2309.01952) | [code](https://github.com/UT-Austin-RPL/TRILL) |
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## Abstract |
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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. |
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## Citing |
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``` |
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@inproceedings{seo2023trill, |
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title={Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation}, |
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author={Seo, Mingyo and Han, Steve and Sim, Kyutae and |
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Bang, Seung Hyeon and Gonzalez, Carlos and |
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Sentis, Luis and Zhu, Yuke}, |
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booktitle={IEEE-RAS International Conference on Humanoid Robots (Humanoids)}, |
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year={2023} |
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} |
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``` |
