Adaptive Complexity Model Predictive Control

TL;DR

This paper presents an adaptive MPC approach that dynamically adjusts model complexity based on task requirements, improving agility and task range while maintaining stability and feasibility guarantees.

Contribution

It introduces a novel adaptive MPC formulation that selectively simplifies models during planning, inspired by behavioral economics and biomechanics, ensuring stability and expanding task capabilities.

Findings

Enables more agile robot motions.

Expands range of executable tasks.

Maintains stability and feasibility.

Abstract

This work introduces a formulation of model predictive control (MPC) which adaptively reasons about the complexity of the model based on the task while maintaining feasibility and stability guarantees. Existing MPC implementations often handle computational complexity by shortening prediction horizons or simplifying models, both of which can result in instability. Inspired by related approaches in behavioral economics, motion planning, and biomechanics, our method solves MPC problems with a simple model for dynamics and constraints over regions of the horizon where such a model is feasible and a complex model where it is not. The approach leverages an interleaving of planning and execution to iteratively identify these regions, which can be safely simplified if they satisfy an exact template/anchor relationship. We show that this method does not compromise the stability and feasibility properties of the system, and measure performance in simulation experiments on a quadrupedal robot executing agile behaviors over terrains of interest. We find that this adaptive method enables more agile motion and expands the range of executable tasks compared to fixed-complexity implementations.