Versatile Real-Time Motion Synthesis via Kino-Dynamic MPC with Hybrid-Systems DDP

TL;DR

This paper introduces a nonlinear MPC framework that enables quadruped robots to perform and switch between diverse agile motions like jumping and trotting in real-time, using hybrid-kinodynamic models and a DDP solver.

Contribution

It presents a unified NMPC approach for on-the-fly re-planning of both specialized and regular motions in quadruped robots, integrating hybrid systems and DDP.

Findings

Successfully performed complex motion sequences on two quadruped platforms.

Demonstrated rapid transition between different agile skills.

Validated effectiveness and generality of the approach.

Abstract

Specialized motions such as jumping are often achieved on quadruped robots by solving a trajectory optimization problem once and executing the trajectory using a tracking controller. This approach is in parallel with Model Predictive Control (MPC) strategies that commonly control regular gaits via online re-planning. In this work, we present a nonlinear MPC (NMPC) technique that unlocks on-the-fly re-planning of specialized motion skills and regular locomotion within a unified framework. The NMPC reasons about a hybrid kinodynamic model, and is solved using a variant of a constrained Differential Dynamic Programming (DDP) solver. The proposed NMPC enables the robot to perform a variety of agile skills like jumping, bounding, and trotting, and the rapid transition between these skills. We evaluated the proposed algorithm with three challenging motion sequences that combine multiple agile skills, on two quadruped platforms, Unitree A1, and MIT Mini Cheetah, showing its effectiveness and generality.