Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots

2026-03-09

AI Summary

This study addresses the challenge of stable payload transportation for quadrupedal robots under unknown or dynamic loads, model uncertainties, and complex terrains. The authors propose a hierarchical planning and control framework: at the high level, a gradient-based indirect adaptive law is integrated with model predictive control (MPC) to online estimate parameters of a reduced-order motion model and generate real-time trajectories; at the low level, a nonlinear whole-body controller tracks these trajectories. The approach innovatively combines indirect adaptation with MPC and incorporates convex stability constraints to ensure convergence of parameter estimation errors. Experimental results demonstrate that the system can transport static unknown payloads up to 109% and 91% of its body weight on flat and rough terrain, respectively, as well as dynamic payloads up to 73% of its weight. Hardware tests confirm robustness against disturbances, obstacles, and outdoor conditions, significantly outperforming conventional MPC and L1-MPC baselines.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.

Problem

• quadrupedal robots
• payload transportation
• model uncertainty
• robust control
• terrain adaptation

Innovation

• adaptive model predictive control
• quadrupedal robot
• indirect adaptive law
• payload transportation
• whole-body control