Abstract
Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for high dynamics locomotion tasks. We demonstrate the capability of our method in simulation and on a real quadruped robot performing trotting and bounding motions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 931-942 |
| Number of pages | 12 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 168 |
| State | Published - 2022 |
| Event | 4th Annual Learning for Dynamics and Control Conference, L4DC 2022 - Stanford, United States Duration: Jun 23 2022 → Jun 24 2022 |
Keywords
- Trajectory optimization
- model based method
- quadruped robot
- value function learning
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability