2022/01/11 by Julian Viereck, Viereck, Julian, Avadesh Meduri +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · #FOS: Computer and information sciences #Fuel Cells and Related Materials #Robotic Locomotion and Control #Robotics (cs.RO) #Viral Infectious Diseases and Gene Expression in Insects
paper · pdf · doi:10.48550/arxiv.2201.04090
openalex publication_date 2022/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.