Trajectory optimization is the core of modern model-based robotic control and motion planning. Existing trajectory optimizers, based on sequential quadratic programming (SQP) or differential dynamic programming (DDP), are often limited by their slow computation efficiency, low modeling flexibility, and poor convergence for complex tasks requiring hard constraints. In this paper, we introduce Hippo, a solver that can handle inequality constraints using the interior-point method (IPM) with an adaptive barrier update strategy and hard equality constraints via projection or IPM. Through extensive numerical benchmarks, we show that Hippo is a robust and efficient alternative to existing state-of-the-art solvers for difficult robotic trajectory optimization problems requiring high-quality solutions, such as locomotion and manipulation.

翻译：轨迹优化是现代基于模型的机器人控制与运动规划的核心。现有的轨迹优化器，如基于序列二次规划（SQP）或微分动态规划（DDP）的方法，通常受限于其计算效率低、建模灵活性差，以及在需要硬约束的复杂任务中收敛性不佳等问题。本文提出Hippo，一种能够处理不等式约束与硬等式约束的求解器：对于不等式约束，它采用带有自适应障碍参数更新策略的内点法（IPM）；对于硬等式约束，则通过投影法或IPM进行处理。通过大量的数值基准测试，我们证明，对于需要高质量解的复杂机器人轨迹优化问题（如运动与操作任务），Hippo是现有先进求解器的一个鲁棒且高效的替代方案。