通过人类遥操作实现仿人机器人移动操作的深度模仿学习
Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
AI总结：
本文提出数据高效框架TRILL，通过VR界面收集人类演示，结合全身控制与高层动作抽象，解决高自由度仿人机器人移动操作技能的深度模仿学习难题，并在仿真和真实机器人上验证了其有效性。
摘要：
我们致力于解决通过深度模仿学习开发仿人机器人移动操作技能的问题。为高自由度仿人机器人收集任务演示和训练策略的困难带来了巨大挑战。我们提出了TRILL，这是一个基于人类演示训练仿人机器人移动操作策略的数据高效框架。在该框架中，我们通过直观的虚拟现实（VR）界面收集人类演示数据。我们采用全身控制公式，将人类操作者的任务空间指令转换为机器人的关节力矩驱动，同时稳定其动力学。通过采用为仿人机器人移动操作量身定制的高层动作抽象，我们的方法能够高效学习复杂的感知运动技能。我们在仿真和真实世界机器人上验证了TRILL在执行各种移动操作任务时的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
