Hippo高效求解带约束的机器人轨迹优化问题，适合复杂运动规划。
Hippo: High-performance Interior-Point and Projection-based Solver for Generic Constrained Trajectory Optimization
- 结合内点法与投影法处理不等式和等式约束
- 自适应障碍更新策略提升求解速度与稳定性
- 在足式行走与操作任务中表现优于现有方法
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。现有基于序列二次规划（SQP）或微分动态规划（DDP）的轨迹优化器常受限于计算效率低、建模灵活性差以及在需硬约束的复杂任务中收敛性不佳。本文提出Hippo，一种可处理不等式约束的内点法（IPM）求解器，采用自适应障碍更新策略，并通过投影或IPM处理硬等式约束。通过大量数值基准测试，我们证明Hippo是解决高难度机器人轨迹优化问题（如步态与操作）的稳健且高效的替代方案，能获得高质量解。
原文摘要 · Abstract (English)
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.
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