An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
This work addresses the challenge of real-time stabilization in dynamic locomotion for humanoid robots, representing an incremental improvement in control methods.
The authors tackled the problem of whole-body dynamic walking control for humanoid robots by implementing a convex quadratic program that respects full robot dynamics and constraints, achieving 1kHz control rates for a 34-DOF humanoid and surpassing off-the-solver performance.
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.