  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.
