UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models
This addresses the challenge of handling multiple constraints in robot motion planning for robotics applications, representing an incremental improvement over existing constrained generative planners.
The paper tackles the problem of robot motion generation with multiple constraints like collision avoidance and dynamic consistency, proposing UniConFlow, a unified flow matching framework that systematically incorporates equality and inequality constraints through a quadratic programming formulation, demonstrating improved safety and feasibility in mobile navigation and high-dimensional manipulation tasks compared to state-of-the-art methods.
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.