Multi-contact Stochastic Predictive Control for Legged Robots with Contact Locations Uncertainty

TL;DR

This paper introduces a stochastic model predictive control approach for legged robots that effectively manages contact location uncertainties, significantly improving safety and success rates over traditional methods.

Contribution

It presents a novel stochastic nonlinear model predictive control framework that explicitly accounts for contact location uncertainties in legged robot trajectory planning.

Findings

SNMPC achieves 100% success in contact safety during simulations.

NMPC fails in 48.3% of motions due to contact violations.

SNMPC outperforms NMPC in safety and reliability.

Abstract

Trajectory optimization under uncertainties is a challenging problem for robots in contact with the environment. Such uncertainties are inevitable due to estimation errors, control imperfections, and model mismatches between planning models used for control and the real robot dynamics. This induces control policies that could violate the contact location constraints by making contact at unintended locations, and as a consequence leading to unsafe motion plans. This work addresses the problem of robust kino-dynamic whole-body trajectory optimization using stochastic nonlinear model predictive control (SNMPC) by considering additive uncertainties on the model dynamics subject to contact location chance-constraints as a function of robot's full kinematics. We demonstrate the benefit of using SNMPC over classic nonlinear MPC (NMPC) for whole-body trajectory optimization in terms of contact location constraint satisfaction (safety). We run extensive Monte-Carlo simulations for a quadruped robot performing agile trotting and bounding motions over small stepping stones, where contact location satisfaction becomes critical. Our results show that SNMPC is able to perform all motions safely with 100% success rate, while NMPC failed 48.3% of all motions.