Humanoid robots have the potential to help human workers by realizing physically demanding manipulation tasks such as moving large boxes within warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This paper presents a framework for DMM via whole-body teleoperation, built upon three key contributions: Firstly, a teleoperation framework employing a Human Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed. Secondly, the study introduces a dynamic locomotion mapping, utilizing human-robot reduced order models, and a kinematic retargeting strategy for manipulation tasks. Additionally, the paper discusses the role of whole-body haptic feedback for wheeled humanoid control. Finally, the system's effectiveness and mappings for DMM are validated through locomanipulation experiments and heavy box pushing tasks. Here we show two forms of DMM: grasping a target moving at an average speed of 0.4 m/s, and pushing boxes weighing up to 105\% of the robot's weight. By simultaneously adjusting their pitch and using their arms, the pilot adjusts the robot pose to apply larger contact forces and move a heavy box at a constant velocity of 0.2 m/s.

翻译：人形机器人有望通过执行诸如在仓库中搬运大型箱子等体力要求高的操控任务来协助人类工人。我们将此类任务定义为动态移动操控（DMM）。本文提出了一种基于全身遥操作的DMM框架，其核心贡献包括三个方面：首先，提出了一种采用人机接口（HMI）与双轮人形机器人SATYRR的遥操作框架；其次，研究引入了基于人机降阶模型的动态运动映射以及面向操控任务的运动学重定向策略；此外，本文探讨了全身触觉反馈在轮式人形机器人控制中的作用；最后，通过运动操控实验和重箱推动任务验证了该系统的有效性及面向DMM的映射关系。本文展示了两种DMM形式：抓取平均速度为0.4米/秒的目标物体，以及推动重量高达机器人自重105%的箱子。操作员通过同时调节机器人俯仰姿态并利用其手臂，调整机器人位姿以施加更大接触力，从而以0.2米/秒的恒定速度推动重箱。