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

TL;DR

A novel framework for legged loco-manipulation that considers whole-body coordination through a hierarchical optimization-based control framework that has been validated in simulation and hardware experiments, highlighting the necessity of whole-body optimization compared to the baseline locomotion MPC when interacting with heavy objects.

Abstract

In recent years, the field of legged robotics has seen growing interest in enhancing the capabilities of these robots through the integration of articulated robotic arms. However, achieving successful loco-manipulation, especially involving interaction with heavy objects, is far from straightforward, as object manipulation can introduce substantial disturbances that impact the robot's locomotion. This paper presents a novel framework for legged loco-manipulation that considers whole-body coordination through a hierarchical optimization-based control framework. First, an online manipulation planner computes the manipulation forces and manipulated object task-based reference trajectory. Then, pose optimization aligns the robot's trajectory with kinematic constraints. The resultant robot reference trajectory is executed via a linear MPC controller incorporating the desired manipulation forces into its prediction model. Our approach has been validated in simulation and hardware experiments, highlighting the necessity of whole-body optimization compared to the baseline locomotion MPC when interacting with heavy objects. Experimental results with Unitree Aliengo, equipped with a custom-made robotic arm, showcase its ability to lift and carry an 8kg payload and manipulate doors.

Figures (7)

• Figure 1: Snapshots of Aliengo lifting and carrying a 5kg payload. Supplemental video: https://youtu.be/0hYDa94F78E
• Figure 2: Block diagram for the proposed framework. Highlighted in green are the novel components that, together with the swing leg controller, form the high-level controller for the quadruped
• Figure 3: Unitree Aliengo with custom-made arm used for experimental validation of the proposed approach
• Figure 4: Improvements using loco-manipulation MPC. In the plots, we compare the results using the loco-manipulation MPC and the baseline MPC. The robot is lifting from the ground a 3Kg object and reaches a predefined arm configuration,
• Figure 5: Improvements using object manipulation planning. In the plots, we compare the results using the loco-manipulation MPC with the object manipulation planner or a fixed manipulation force without the object manipulation planner. The robot lifts a 10 Kg object from the ground and reaches a predefined arm configuration in shorter times, making the lifting more dynamic.