arXiv AI
ConicFlow: constrained Inference-time optimal control for flow matching models
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arXiv AI
Oct 12
UniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
UniConFlow is a unified constrained flow‑matching framework that generates robot trajectories while systematically handling both equality and inequality constraints such as collision avoidance, actuation limits, and dynamic consistency. It introduces a prescribed‑time zeroing function to shape a time‑varying guidance field during inference, and employs a violation‑segment extraction protocol and trajectory compression to address computational challenges in long‑horizon, high‑dimensional planning. Experiments on a double inverted pendulum, a real‑to‑sim car racing task, and a sim‑to‑real manipulation task show that UniConFlow outperforms state‑of‑the‑art generative planners and conventional optimization baselines on certified motion planning metrics like safety, kinodynamic consistency, and action feasibility.
arXiv AI
Oct 5
MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching
arXiv:2610.02260v1 Announce Type: new Abstract: Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as ob...
arXiv Machine Learning
Jun 16
DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
arXiv Machine Learning
Oct 12
Primal-Dual Flow Matching for Sample-Wise Constrained Generation
The paper introduces Primal-Dual Flow Matching (PDFM), a method for learning data generators that satisfy user‑specified sample‑wise constraints with a guaranteed probability of satisfaction. PDFM builds on a population‑level constrained negative log‑likelihood objective and a flow‑matching parameterization, providing theoretical guarantees under regularity and expressivity assumptions. Practically, PDFM requires only binary membership oracle feedback, avoids differentiable distance metrics or convexity assumptions, and empirically outperforms vanilla flow matching while maintaining distributional fidelity.
arXiv Machine Learning
Jun 19
Meta Flow Maps enable scalable reward alignment
arXiv:2601. 14430v2 Announce Type: replace-cross Abstract: Controlling generative models is computationally expensive.
arXiv Machine Learning
Aug 4
Provably Safe Generative Sampling with Constricting Barrier Functions
arXiv:2602. 21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions.