发表机构
The Hong Kong University of Science and Technology (Guangzhou); Korea Advanced Institute of Science and Technology; ETH Zurich（香港科技大学（广州）; 韩国科学技术院; 苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对高自由度机器人全身移动操作面临的挑战，提出将其分解为部分参考运动生成和低级模仿控制的框架，用KNF模型生成参考运动，经高低级控制器实现精确控制，在模拟和硬件实验中均表现优异，为相关操作提供实用方案。
AI 中文摘要
移动操作展现出了很有前景的能力。然而，实现高精度控制、管理由多自由度引发的高维动作空间以及充分利用全身系统的固有冗余仍具有挑战性。本文提出了一种新颖的全身控制框架，通过将复杂的移动操作问题分解为部分参考运动生成和低级模仿控制来有效应对这些挑战。引入了一种在大规模运动学数据集上训练的新运动学归一化流（KNF）模型来生成多样且可行的部分参考运动。训练了高级控制器在KNF的潜在空间中导航以利用冗余解，低级控制器确保物理上可行且精确的运动执行。在配备六自由度机械臂的四足机器人上验证了该方法。模拟实验结果表明该方法在跟踪精度和可行工作空间覆盖方面显著优于现有方法。硬件部署评估中，系统在8种不同的移动操作任务的24个情节上实现了末端执行器姿态跟踪误差为4.5厘米和0.14弧度，同时分别以0.1米/秒和0.01弧度/秒的线性和角速度误差保持精确的运动跟踪，优于竞争基线。我们的方法为高自由度机器人系统中的精确和通用全身移动操作提供了实用且强大的解决方案，对各种下游机器人任务具有潜在的应用前景。
英文摘要
Loco-manipulation has recently shown promising capabilities; however, achieving high-precision control, managing the high-dimensional action space induced by many degrees of freedom (DoFs), and fully exploiting the inherent redundancy of whole-body systems remain challenging. In this paper, we propose a novel whole-body control framework that effectively addresses these challenges by decomposing the complex loco-manipulation problem into partial reference motion generation and low-level imitation control. We introduce a new Kinematic Normalizing Flow (KNF) model, trained on a large-scale kinematic dataset, that generates diverse yet feasible partial reference motions. A high-level controller is then trained to navigate the KNF's latent space to exploit redundant solutions, while a low-level controller ensures physically feasible and accurate motion execution. We validate our approach on the quadrupedal robot equipped with a six-DoF robotic arm. In simulation, experimental results show that our approach significantly outperforms state-of-the-art methods in terms of tracking accuracy and feasible workspace coverage. For hardware deployment, we evaluate the system over 24 episodes across 8 different mobile loco-manipulation tasks. The system achieves end-effector pose-tracking errors of 4.5 cm and 0.14 rad, while maintaining accurate locomotion tracking with linear and angular velocity errors of 0.1 m/s and 0.01 rad/s, respectively, outperforming competitive baselines. Our method represents a practical and powerful solution for accurate and generalized whole-body loco-manipulation in high-DoF robotic systems, with promising potential for diverse downstream robotic tasks.
