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.

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In this paper, we present a novel methodology to introduce adaptive control for force-based control systems, with application to legged robots. In our approach, the reference model is based on the quadratic program force control. We evaluate our proposed control design on a high-fidelity physical simulation of LASER, a dynamic quadruped robot. Our proposed method guarantees input-to-state stability and is successfully validated for the problem of quadruped robots walking on rough terrain while carrying unknown and timevarying loads.