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In recent years, the field of legged robotics has seen growing interest inenhancing the capabilities of these robots through the integration ofarticulated robotic arms. However, achieving successful loco-manipulation,especially involving interaction with heavy objects, is far fromstraightforward, as object manipulation can introduce substantial disturbancesthat impact the robot's locomotion. This paper presents a novel framework forlegged loco-manipulation that considers whole-body coordination through ahierarchical optimization-based control framework. First, an onlinemanipulation planner computes the manipulation forces and manipulated objecttask-based reference trajectory. Then, pose optimization aligns the robot'strajectory with kinematic constraints. The resultant robot reference trajectoryis executed via a linear MPC controller incorporating the desired manipulationforces into its prediction model. Our approach has been validated in simulationand hardware experiments, highlighting the necessity of whole-body optimizationcompared to the baseline locomotion MPC when interacting with heavy objects.Experimental results with Unitree Aliengo, equipped with a custom-made roboticarm, showcase its ability to successfully lift and carry an 8kg payload andmanipulate doors.

Hierarchical Optimization-based Control for Whole-body Loco-manipulation of Heavy Objects