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，但四足机器人执行有效的操控任务仍难以实现。本文提出一种能够处理真实世界协同操控任务（包括由物体属性、形状及地形引发的不确定性）的分层控制系统。该方法包含三个控制层级：首先，自适应控制器在不预先获知物体属性与地形信息的情况下，计算操控物体所需的力与力矩；随后，基于二次规划(QP)的控制器将计算所得力与力矩在四足机器人团队间进行最优分配，该控制器在满足机器人-物体接触约束的同时优化各机器人接触点的位置；最后，为每台机器人设计分散式移动操控控制器，使其在维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——四足机器人团队在不同地形上操控重达18千克的未知物体，并沿期望轨迹行进。