UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models

2025-06-03

AI Summary

To address the challenge of jointly satisfying multiple constraints—collision avoidance, dynamical consistency, and actuation limits—in robot motion planning, this paper proposes a unified constraint-aware trajectory generation framework based on flow matching. Methodologically: (i) we introduce a preset-time vanishing function to enhance inference flexibility during trajectory generation; (ii) we formulate both equality and inequality constraints as differentiable quadratic programming (QP)-derived guidance signals—a first in generative planning—enabling certified constraint satisfaction; (iii) constraints are implicitly embedded into the diffusion process, enabling end-to-end feasible trajectory synthesis without auxiliary controllers or model retraining. Evaluated on mobile navigation and high-dimensional dexterous manipulation tasks, our approach significantly improves trajectory safety and feasibility, outperforming state-of-the-art constrained generative planners in both constraint violation rates and task success rates.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of constraints, such as collision avoidance and dynamic consistency, which are often treated separately or only partially considered. This paper proposes UniConFlow, a unified flow matching (FM) based framework for trajectory generation that systematically incorporates both equality and inequality constraints. UniConFlow introduces a novel prescribed-time zeroing function to enhance flexibility during the inference process, allowing the model to adapt to varying task requirements. To ensure constraint satisfaction, particularly with respect to obstacle avoidance, admissible action range, and kinodynamic consistency, the guidance inputs to the FM model are derived through a quadratic programming formulation, which enables constraint-aware generation without requiring retraining or auxiliary controllers. We conduct mobile navigation and high-dimensional manipulation tasks, demonstrating improved safety and feasibility compared to state-of-the-art constrained generative planners. Project page is available at https://uniconflow.github.io.

Problem

• Handles multiple constraints in robot motion planning
• Unifies equality and inequality constraints in trajectory generation
• Ensures safety and feasibility without retraining or auxiliary controllers

Innovation

• Unified flow matching for constrained trajectory generation
• Prescribed-time zeroing function enhances inference flexibility
• Quadratic programming ensures constraint satisfaction without retraining