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We present a new solver for non-convex trajectory optimization problems thatis specialized for robotics applications. CALIPSO, or the Conic AugmentedLagrangian Interior-Point SOlver, combines several strategies for constrainednumerical optimization to natively handle second-order cones andcomplementarity constraints. It reliably solves challenging motion-planningproblems that include contact-implicit formulations of impacts and Coulombfriction and state-triggered constraints where general-purpose non-convexsolvers like SNOPT and Ipopt fail to converge. Additionally, CALIPSO supportsefficient differentiation of solutions with respect to problem data, enablingbi-level optimization applications like auto-tuning of feedback policies.Reliable convergence of the solver is demonstrated on a range of problems frommanipulation, locomotion, and aerospace domains. An open-source implementationof this solver is available.

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