Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots

TL;DR

This paper introduces a stochastic trajectory optimization method for legged robots that enhances robustness against uncertainties, reducing foot slippage and improving motion safety compared to deterministic approaches.

Contribution

The work presents a novel stochastic optimization framework that accounts for model and contact uncertainties, improving the robustness of centroidal momentum trajectories in legged robots.

Findings

Reduces foot slippage in simulated quadruped robot gaits.

Achieves better performance over deterministic planning under uncertainties.

Demonstrates robustness against unmodelled contact interactions.

Abstract

Generation of robust trajectories for legged robots remains a challenging task due to the underlying nonlinear, hybrid and intrinsically unstable dynamics which needs to be stabilized through limited contact forces. Furthermore, disturbances arising from unmodelled contact interactions with the environment and model mismatches can hinder the quality of the planned trajectories leading to unsafe motions. In this work, we propose to use stochastic trajectory optimization for generating robust centroidal momentum trajectories to account for additive uncertainties on the model dynamics and parametric uncertainties on contact locations. Through an alternation between the robust centroidal and whole-body trajectory optimizations, we generate robust momentum trajectories while being consistent with the whole-body dynamics. We perform an extensive set of simulations subject to different uncertainties on a quadruped robot showing that our stochastic trajectory optimization problem reduces the amount of foot slippage for different gaits while achieving better performance over deterministic planning.