An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion

Abstract

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.

6 pages, published at ICRA 2014

Cited by in corpus (10)

• Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
• Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas
• Walking on Partial Footholds Including Line Contacts with the Humanoid Robot Atlas
• Efficient Multi-Contact Pattern Generation with Sequential Convex Approximations of the Centroidal Dynamics
• Straight-Leg Walking Through Underconstrained Whole-Body Control
• Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization
• Automatic Gain Tuning of a Momentum Based Balancing Controller for Humanoid Robots
• Learning Task-Specific Dynamics to Improve Whole-Body Control
• Model Predictive Control with Environment Adaptation for Legged Locomotion
• $\mathcal{N}$IPM-HLSP: An Efficient Interior-Point Method for Hierarchical Least-Squares Programs