论文标题
Calipso：具有圆锥和互补限制的轨迹优化的可区分求解器
CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints
论文作者
论文摘要
我们提出了一个新的求解器，用于专门用于机器人应用程序的非凸轨迹优化问题。 Calipso或Conic增强Lagrangian内点求解器，结合了几种约束数值优化的策略，以本地处理二阶锥体和互补性约束。它可靠地解决了具有挑战性的运动规划问题，其中包括影响和库仑摩擦的接触率公式和库仑摩擦的制定以及国家触发的约束，在这些限制中，通用的非convex求解器（如Snopt和Ipopt）无法收敛。此外，Calipso支持有关问题数据的有效分化，从而实现了双层优化应用程序，例如自动调整反馈策略。求解器的可靠收敛性在操纵，运动和航空航天域的一系列问题上得到了证明。该求解器的开源实现可用。
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.
