CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints
Abstract
We present a new solver for non-convex trajectory optimization problems that is specialized for robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point SOlver, combines several strategies for constrained numerical optimization to natively handle second-order cones and complementarity constraints. It reliably solves challenging motion-planning problems that include contact-implicit formulations of impacts and Coulomb friction and state-triggered constraints where general-purpose non-convex solvers like SNOPT and Ipopt fail to converge. Additionally, CALIPSO supports efficient differentiation of solutions with respect to problem data, enabling bi-level optimization applications like auto-tuning of feedback policies. Reliable convergence of the solver is demonstrated on a range of problems from manipulation, locomotion, and aerospace domains. An open-source implementation of this solver is available.
Cite
@article{arxiv.2205.09255,
title = {CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints},
author = {Taylor A. Howell and Simon Le Cleac'h and Kevin Tracy and Zachary Manchester},
journal= {arXiv preprint arXiv:2205.09255},
year = {2023}
}
Comments
Fixes and minor reformatting