Hippo: High-performance Interior-Point and Projection-based Solver for Generic Constrained Trajectory Optimization

TL;DR

Hippo is a new high-performance solver for robotic trajectory optimization that efficiently handles complex constraints using interior-point and projection methods, outperforming existing solvers in speed and robustness.

Contribution

This paper introduces Hippo, a novel trajectory optimizer combining interior-point and projection techniques, offering improved efficiency and flexibility over traditional SQP and DDP methods.

Findings

Hippo outperforms state-of-the-art solvers in benchmark tests.

It handles complex constraints with high robustness.

Provides high-quality solutions for locomotion and manipulation tasks.

Abstract

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.