Real-Time Multi-Contact Model Predictive Control via ADMM
This addresses the challenge of real-time control in robotics for tasks requiring contact initiation, offering a solution that avoids the need for pre-scheduled modes or excessive computational complexity.
The paper tackles the problem of real-time control for systems with contact events, such as locomotion and manipulation, by proposing a hybrid model predictive control algorithm called consensus complementarity control (C3), which achieves high-speed reasoning over potential contacts using ADMM and parallelization, validated on numerical examples and physical experiments.
We propose a hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are so computationally complex that they cannot run at real-time rates. We present a method, based on the alternating direction method of multipliers (ADMM), capable of highspeed reasoning over potential contact events. Via a consensus formulation, our approach enables parallelization of the contact scheduling problem. We validate our results on three numerical examples, including two frictional contact problems, and physical experimentation on an underactuated multi-contact system.