Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.

迄今为止，对多机器人系统的机器人研究主要集中在实现智能群体行为和非接触式人类交互上。相比之下，触觉或物理人机交互的研究主要集中在单个机器人提供的帮助上。因此，我们对通过人类与多个协作机器人团队之间的接触力进行的物理交互和隐式通信的理解是有限的。我们在这里介绍术语“物理人类多机器人协作”(PHMRC) 来描述这种更复杂的情况，我们认为这与未来的服务机器人技术高度相关。本文中讨论的场景涵盖多个靠近并通过物理接触耦合的机械手。我们将这组机器人表示为放大的敏捷机器人手的手指。这种观点使我们能够采用基于模型的抓取理论来处理多接触情况。我们的扭矩控制方法将灵巧的多机械手抓取技能、接触力优化、物体动力学补偿和先进的阻抗调节集成到一个连贯的兼容控制方案中。为此，我们做出了根本的理论改进。最后，最多四个协作 KUKA LWR IV+ 机械手在模拟和现实世界中进行的实验验证了基于模型的控制方法。作为副作用，我们注意到我们的多机械手控制框架同样适用于多腿系统，我们也在四足动物 ANYmal 上执行它，受非共面接触和人类交互影响。