Variable Horizon MPC with Swing Foot Dynamics for Bipedal Walking Control

TL;DR

This paper introduces a two-level variable horizon MPC framework for bipedal walking that optimizes swing foot landing and timing, improving stability and robustness through real robot experiments.

Contribution

The novel VH-MPC framework integrates swing foot dynamics and variable horizon planning for enhanced bipedal locomotion control.

Findings

Effective stabilization of CoM dynamics during walking

Successful implementation on the Bolt robot with disturbances

Robust walking patterns demonstrated in simulations and experiments

Abstract

In this paper, we present a novel two-level variable Horizon Model Predictive Control (VH-MPC) framework for bipedal locomotion. In this framework, the higher level computes the landing location and timing (horizon length) of the swing foot to stabilize the unstable part of the center of mass (CoM) dynamics, using feedback from the CoM states. The lower level takes into account the swing foot dynamics and generates dynamically consistent trajectories for landing at the desired time as close as possible to the desired location. To do that, we use a simplified model of the robot dynamics projected in swing foot space that takes into account joint torque constraints as well as the friction cone constraints of the stance foot. We show the effectiveness of our proposed control framework by implementing robust walking patterns on our torque-controlled and open-source biped robot, Bolt. We report extensive simulations and real robot experiments in the presence of various disturbances and uncertainties.