A Nonlinear MPC Framework for Loco-Manipulation of Quadrupedal Robots with Non-Negligible Manipulator Dynamics

2025-07-29

AI Summary

Quadrupedal robots equipped with high-dynamics manipulators suffer from strong motion-manipulation coupling, high-dimensional dynamics models, and challenges in real-time control. Method: This paper proposes a hierarchical decoupled nonlinear model predictive control (NMPC) framework. It decouples the single-rigid-body template model of the robot body from the full-order dynamics of the manipulator—simplifying torso dynamics via a motion template while preserving high-fidelity manipulator dynamics—and integrates both into a synergistic architecture combining 60-Hz NMPC trajectory optimization with a 500-Hz nonlinear whole-body controller. Contribution/Results: The framework unifies high-accuracy manipulation dynamics modeling with real-time computational efficiency. Extensive simulation and hardware experiments on the Unitree Go2 + Kinova platform demonstrate robust performance across diverse mobile manipulation tasks under payload disturbances, external perturbations, and uneven terrain, significantly improving both robustness and real-time capability.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Model predictive control (MPC) combined with reduced-order template models has emerged as a powerful tool for trajectory optimization in dynamic legged locomotion. However, loco-manipulation tasks performed by legged robots introduce additional complexity, necessitating computationally efficient MPC algorithms capable of handling high-degree-of-freedom (DoF) models. This letter presents a computationally efficient nonlinear MPC (NMPC) framework tailored for loco-manipulation tasks of quadrupedal robots equipped with robotic manipulators whose dynamics are non-negligible relative to those of the quadruped. The proposed framework adopts a decomposition strategy that couples locomotion template models -- such as the single rigid body (SRB) model -- with a full-order dynamic model of the robotic manipulator for torque-level control. This decomposition enables efficient real-time solution of the NMPC problem in a receding horizon fashion at 60 Hz. The optimal state and input trajectories generated by the NMPC for locomotion are tracked by a low-level nonlinear whole-body controller (WBC) running at 500 Hz, while the optimal torque commands for the manipulator are directly applied. The layered control architecture is validated through extensive numerical simulations and hardware experiments on a 15-kg Unitree Go2 quadrupedal robot augmented with a 4.4-kg 4-DoF Kinova arm. Given that the Kinova arm dynamics are non-negligible relative to the Go2 base, the proposed NMPC framework demonstrates robust stability in performing diverse loco-manipulation tasks, effectively handling external disturbances, payload variations, and uneven terrain.

Problem

• Develops NMPC for quadruped robots with significant manipulator dynamics
• Addresses real-time control challenges in loco-manipulation tasks
• Ensures stability under disturbances and varying payloads/terrain

Innovation

• Nonlinear MPC for quadruped loco-manipulation tasks
• Decomposition strategy coupling SRB and full-order manipulator
• Layered control with real-time NMPC and WBC