ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation

2026-07-15

AI Summary

This work addresses the inconsistency between training and inference constraints in robot motion generation by proposing ConFlow, a novel framework that integrates constraint guidance directly into flow matching. ConFlow explicitly models task constraints through differentiable obstacle or cost functions and replaces the standard Gaussian prior with a conditional Gaussian process to enforce trajectory smoothness and boundary conditions. Additionally, it leverages infeasible trajectories as negative supervision signals to enhance constraint adherence. Experimental results demonstrate that ConFlow significantly reduces collision rates and improves trajectory quality in dual-robot navigation tasks, outperforming existing flow matching approaches both with and without inference-time guidance.

Abstract

In recent years Flow Matching has become a prominent method for generative modeling robot motion generation. In its generic form Flow Matching is an ODE-based neural sampler that is trained by regressing empirical flow fields associated with motion samples as data. However, in robot motion generation we often have additional constraints that might not be present in the collected data. The majority of current approaches train the flow on the available data and use inference-time guidance to enforce task-specific constraints. To address this mismatch, we propose \textbf{ConFlow}, a constraint-guided flow matching framework that incorporates constraint information directly into the training objective via differentiable barrier or cost functions. To address design specifications such as smoothness and boundary conditions, we propose replacing the standard Gaussian source distribution used in flow matching training with a conditional Gaussian Process. Our approach also uses infeasible demonstrations as negative supervision, improving constraint satisfaction without requiring additional expert data. Experiments on a two-robot navigation task demonstrate that ConFlow achieves lower collision rates and higher trajectory quality than standard flow matching baselines, with or without inference-time guidance. These results validate training-time constraint integration as an effective approach to closing the training--inference gap in generative motion models.

Problem

• Flow Matching
• Motion Generation
• Constraints
• Training-Inference Gap
• Robotics

Innovation

• Flow Matching
• Constraint-Guided Learning
• Gaussian Process
• Negative Supervision
• Motion Generation