ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
This paper proposes ConFlow, a framework that incorporates constraint information directly into the flow matching training objective via differentiable barrier or cost functions and a conditional Gaussian Process, improving constraint satisfaction and trajectory quality in robot motion generation. Experiments on a two-robot navigation task demonstrate lower collision rates and higher trajectory quality compared to standard flow matching baselines.
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[Submitted on 15 Jul 2026]
Title:ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation
View a PDF of the paper titled ConFlow: Constraints-Guided Learning with Flow Matching for Motion Generation, by Nutan Chen and Jianxiang Feng and Marvin Alles and Botond Cseke
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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.
Comments: RSS 2026 Workshop on Diffusion for Robot Learning
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.14424 [cs.RO]
(or arXiv:2607.14424v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.14424
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nutan Chen Ph.D. [view email] [v1] Wed, 15 Jul 2026 23:27:04 UTC (280 KB)
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