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

Understand

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.