VRテレオペレーションによるヒューマノイド移動操作の深層模倣学習
Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
VRインタフェースで収集した人間のデモンストレーションから、全身制御と高レベル行動抽象化を用いてヒューマノイドの移動操作スキルを効率的に学習するフレームワークTRILLを提案した。
著者: Mingyo Seo, Steve Han, Kyutae Sim, Seung Hyeon Bang, Carlos Gonzalez, Luis Sentis, Yuke Zhu
分類: cs.RO
原文アブストラクト
We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The difficulty of collecting task demonstrations and training policies for humanoids with a high degree of freedom presents substantial challenges. We introduce TRILL, a data-efficient framework for training 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 by human operators into the robot's joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid loco-manipulation, our method can efficiently learn complex sensorimotor skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various loco-manipulation tasks. Videos and additional materials can be found on the project page: https://ut-austin-rpl.github.io/TRILL.
