We describe a whole-body dynamic walking controller implemented as a convex quadratic program. The controller solves an optimal control problem using an approximate value function derived from a simple walking model while respecting the dynamic, input, and contact constraints of the full robot dynamics. By exploiting sparsity and temporal structure in the optimization with a custom active-set algorithm, we surpass the performance of the best available off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We describe applications to balancing and walking tasks using the simulated Atlas robot in the DARPA Virtual Robotics Challenge.
@article{arxiv.1311.1839,
title = {An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion},
author = {Scott Kuindersma and Frank Permenter and Russ Tedrake},
journal= {arXiv preprint arXiv:1311.1839},
year = {2017}
}