Despite the potential benefits of collaborative robots, effective manipulation tasks with quadruped robots remain difficult to realize. In this paper, we propose a hierarchical control system that can handle real-world collaborative manipulation tasks, including uncertainties arising from object properties, shape, and terrain. Our approach consists of three levels of controllers. Firstly, an adaptive controller computes the required force and moment for object manipulation without prior knowledge of the object's properties and terrain. The computed force and moment are then optimally distributed between the team of quadruped robots using a Quadratic Programming (QP)-based controller. This QP-based controller optimizes each robot's contact point location with the object while satisfying constraints associated with robot-object contact. Finally, a decentralized loco-manipulation controller is designed for each robot to apply manipulation force while maintaining the robot's stability. We successfully validated our approach in a high-fidelity simulation environment where a team of quadruped robots manipulated an unknown object weighing up to 18 kg on different terrains while following the desired trajectory.

翻译：尽管协作机器人具有潜在优势，但利用四足机器人实现有效操控任务仍难以实现。本文提出一种分层控制系统，可处理实际协作操控任务，包括由物体属性、形状和地形引起的不确定性。我们的方法由三层控制器组成。首先，自适应控制器在无需物体属性和地形先验知识的情况下计算操控物体所需的作用力与力矩。随后，通过基于二次规划（QP）的控制器将该作用力和力矩在四足机器人团队间进行最优分配。该QP控制器在满足机器人-物体接触相关约束的同时，优化每台机器人与物体的接触点位置。最后，为每台机器人设计分散式运动操控控制器，使其在保持自身稳定性的同时施加操控力。我们在高保真仿真环境中成功验证了该方法：一组四足机器人操控重达18千克的未知物体，在不同地形上沿期望轨迹移动。