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.

翻译：我们致力于解决通过深度模仿学习发展人形机器人全身操控技能的问题。由于人形机器人具有高度自由度，收集任务示范和训练策略的困难构成了重大挑战。本文提出TRILL框架——一种从人类示范中高效训练人形机器人全身操控策略的数据驱动方法。该框架通过直观的虚拟现实（VR）界面收集人类示范数据，采用全身控制公式将人类操作员的任务空间指令转化为机器人关节力矩驱动，同时稳定其动力学特性。通过采用针对人形机器人全身操控定制的高层动作抽象，该方法能够高效学习复杂的感知运动技能。我们在仿真环境和真实机器人上验证了TRILL在执行多种全身操控任务中的有效性。相关视频及补充材料详见项目页面：https://ut-austin-rpl.github.io/TRILL。