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.
By Zewen Yang, Xiaobing Dai, Dian Yu, Zhijun Li, Majid Khadiv, Sandra Hirche, Sami Haddadin
arXiv:2607. 14424v1 Announce Type: cross Abstract: In recent years Flow Matching has become a prominent method for generative modeling robot motion generation.
By Nutan Chen, Jianxiang Feng, Marvin Alles, Botond Cseke
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.
By Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
The paper introduces Coupled Guidance, a flow‑matching‑specific guidance mechanism that addresses instability issues arising from directly adapting diffusion guidance methods. By leveraging the coupling structure inherent to flow‑matching training, the authors provide a theoretical analysis showing that naive diffusion guidance breaks the training distribution and weakens control. Extensive experiments on robotic collision avoidance, dexterous manipulation, and image generation tasks demonstrate that Coupled Guidance improves sample quality and guidance stability without extra computational overhead.
arXiv:2607. 18200v1 Announce Type: cross Abstract: Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias.
By Junyi Hu, Shuaihang Yuan, Geeta Chandra Raju Bethala, Anthony Tzes, Yi Fang
arXiv:2609.14261v1 Announce Type: cross
Abstract: Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative...
By Prajwal Koirala, Mark Campbell
arXiv:2608.29107v1 Announce Type: new
Abstract: While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. C...
By Avishag Nevo, Tamir Hazan
The paper introduces an oracle-guided differentiable safeguard for flow-matching robot policies that ensures safety constraints are met without explicit constraint models. By using binary feasibility queries during both forward and backward passes, the method corrects unsafe updates and incorporates local boundary information into learning signals. Experiments on obstacle-aware robotic manipulation show that the approach achieves 100% constraint satisfaction while maintaining task performance.
By Jianming Ma, Rongjun Jin, Yizhi Chen, Yue Gao
arXiv:2609. 11697v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment.
By Jianming Ma, Rongjun Jin, Xiaxi Si, Yang Zhang, Yiheng Li, Yue Gao
arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.
By Jingdong Zhang, Xinze Li, Yize Jiang, Luan Yang, Minkai Xu, Junhong Liu
arXiv:2602.08054v2 Announce Type: replace-cross
Abstract: Offline reinforcement learning (RL) offers a promising framework for deploying autonomous systems in safety-critical settings without the ris...
By Manan Tayal, Mumuksh Tayal, Ravi Prakash
arXiv:2609.13231v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provid...
By Manan Tayal, Akshay Nambi