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

2026-02-28

AI Summary

This work addresses the challenges of computational inefficiency, limited modeling flexibility, and poor convergence commonly encountered by existing trajectory optimizers when handling complex robotic tasks with hard constraints. To overcome these limitations, the authors propose Hippo, a novel solver that innovatively integrates interior-point and projected methods to uniformly handle both equality and inequality constraints. Hippo further incorporates an adaptive barrier parameter update strategy, significantly enhancing solution robustness and generalizability. Experimental evaluations across a range of complex robotic trajectory optimization tasks demonstrate that Hippo consistently outperforms state-of-the-art solvers in terms of computational efficiency, convergence stability, and trajectory quality.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• trajectory optimization
• hard constraints
• computational efficiency
• convergence
• modeling flexibility

Innovation

• interior-point method
• projection-based solver
• trajectory optimization
• adaptive barrier update
• hard constraints