Accelerating Model Predictive Control for Legged Robots through Distributed Optimization
🤖 AI Summary
To address the high computational burden of whole-body model predictive control (MPC) for real-time motion control of legged robots—hindering fast closed-loop response—this paper proposes a distributed MPC framework based on the alternating direction method of multipliers (ADMM). This work is the first to apply ADMM to whole-body dynamic MPC for legged robots, enabling subsystem decoupling via dynamics decomposition and enforcing consensus-based coordination. The framework supports modular extensibility—for instance, seamless integration of robotic arms—while guaranteeing strict equivalence to the centralized MPC solution. Experimental validation on two increasingly complex systems demonstrates substantial computational speedup: maximum reduction in solve time reaches 75%, with no compromise in control accuracy or scalability.
Application Category
📝 Abstract
This paper presents a novel approach to enhance Model Predictive Control (MPC) for legged robots through Distributed Optimization. Our method focuses on decomposing the robot dynamics into smaller, parallelizable subsystems, and utilizing the Alternating Direction Method of Multipliers (ADMM) to ensure consensus among them. Each subsystem is managed by its own Optimal Control Problem, with ADMM facilitating consistency between their optimizations. This approach not only decreases the computational time but also allows for effective scaling with more complex robot configurations, facilitating the integration of additional subsystems such as articulated arms on a quadruped robot. We demonstrate, through numerical evaluations, the convergence of our approach on two systems with increasing complexity. In addition, we showcase that our approach converges towards the same solution when compared to a state-of-the-art centralized whole-body MPC implementation. Moreover, we quantitatively compare the computational efficiency of our method to the centralized approach, revealing up to a 75% reduction in computational time. Overall, our approach offers a promising avenue for accelerating MPC solutions for legged robots, paving the way for more effective utilization of the computational performance of modern hardware. Accompanying video at https://youtu.be/Yar4W-Vlh2A. The related code can be found at https://github.com/iit-DLSLab/DWMPC
Problem
Research questions and friction points this paper is trying to address.
legged-robot
predictive-control
acceleration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Distributed Optimization
Model Predictive Control (MPC)
Alternating Direction Method of Multipliers (ADMM)
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