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CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints

2022-05-19 00:19:46
Taylor A. Howell, Simon Le Cleac'h, Kevin Tracy, Zachary Manchester
arXiv_RO
     
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, thrust limits subject to conic constraints, and state-triggered constraints where general-purpose nonlinear programming 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.

Abstract (translated)

URL

https://arxiv.org/abs/2205.09255

PDF

https://arxiv.org/pdf/2205.09255.pdf

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