Hierarchical Control Framework for Collision-Free Collaborative Loco-manipulation of Large and Heavy Objects

Collaborative loco-manipulation by multiple quadruped manipulators enables handling bulky, heavy objects beyond the capabilities of individual robots. However, coordinating robot teams while navigating complex terrains and avoiding obstacles remains challenging. We propose a hierarchical control framework consisting of a model predictive control (MPC)-based manipulation planner with integrated obstacle avoidance, a geometry-aware mapping converting object trajectories into robot commands, and decentralized loco-manipulation MPC controllers. The framework supports collision-free collaborative manipulation tasks and enhances payload capacities. Validation through simulation and real-world hardware experiments with diverse quadruped robot teams demonstrates the approach’s effectiveness, robustness, and practical applicability.