ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation

2026-03-07

AI Summary

This work addresses the challenge of cooperative heavy-load transportation by multi-legged robots, where existing centralized approaches suffer from poor scalability and decentralized strategies often neglect dynamic coupling and interaction forces, leading to overly conservative behavior. To overcome these limitations, the authors propose a distributed model predictive control framework based on the Alternating Direction Method of Multipliers (ADMM). The method explicitly models load-induced dynamic coupling, decomposes the global optimization problem into individual subproblems subject to consensus constraints, and integrates receding-horizon optimization with whole-body control that accounts for sensed interaction forces. This approach achieves high coordination accuracy while significantly improving scalability and real-time performance. Simulations demonstrate rapid convergence within only a few ADMM iterations and robustness to model uncertainties, validated on systems with up to four robots.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Distributed Problem Solving
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots operating in complex, unstructured environments. Centralized planning methods, e.g., holistic trajectory optimization, capture dynamic coupling among robots and payloads but scale poorly with system size, limiting real-time applicability. In contrast, hierarchical and fully decentralized approaches often neglect force and dynamic interactions, leading to conservative behavior. This study proposes an Alternating Direction Method of Multipliers (ADMM)-based distributed model predictive control framework for collaborative loco-manipulation with a team of quadruped robots with manipulators. By exploiting the payload-induced coupling structure, the global optimal control problem is decomposed into parallel individual-robot-level subproblems with consensus constraints. The distributed planner operates in a receding-horizon fashion and achieves fast convergence, requiring only a few ADMM iterations per planning cycle. A wrench-aware whole-body controller executes the planned trajectories, tracking both motion and interaction wrenches. Extensive simulations with up to four robots demonstrate scalability, real-time performance, and robustness to model uncertainty.

Problem

• collaborative loco-manipulation
• distributed model predictive control
• dynamic coupling
• scalability
• real-time performance

Innovation

• ADMM
• distributed model predictive control
• loco-manipulation
• whole-body control
• collaborative robotics