CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints
نویسندگان
چکیده
We present a new solver for non-convex trajectory optimization problems that is specialized robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point SOlver, combines several strategies constrained numerical to natively handle second-order cones and complementarity constraints. It reliably solves challenging motion planning include contact-implicit formulations of impacts Coulomb friction, thrust limits subject conic constraints, state-triggered constraints where general-purpose nonlinear programming solvers like SNOPT Ipopt fail converge. Additionally, CALIPSO supports efficient differentiation solutions with respect problem data, enabling bi-level applications auto-tuning feedback policies. Reliable convergence demonstrated on range from manipulation, locomotion, aerospace domains. An open-source implementation this available.
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ژورنال
عنوان ژورنال: Springer proceedings in advanced robotics
سال: 2023
ISSN: ['2511-1256', '2511-1264']
DOI: https://doi.org/10.1007/978-3-031-25555-7_34