BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning

TL;DR

BiConMP is a nonlinear MPC framework enabling real-time whole-body motion planning for legged robots, effectively handling complex dynamics, terrain variations, and transitions between gaits both in simulation and on real hardware.

Contribution

The paper introduces BiConMP, a novel nonlinear MPC framework that efficiently exploits robot dynamics structure for online whole-body motion planning in legged robots.

Findings

Successfully generated cyclic gaits on a real quadruped robot.

Demonstrated non-trivial acyclic dynamic motions in real-world experiments.

Analyzed the impact of planning horizon and frequency on performance.

Abstract

Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear MPC framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes and transitioning online between different gaits. Further, the ability of BiConMP to generate non-trivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.