arXiv AI

VINE: Taming Generative Control Policies for Reinforcement Learning

arXiv:2607. 10369v1 Announce Type: cross Abstract: Flow-matching policies have emerged as an effective policy parameterization for robot learning.

arXiv Machine Learning

Sep 15

VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching

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

diffusion reinforcement-learning robotics efficiency multimodal

arXiv AI

Jun 10

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

arXiv:2606. 11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.

By Zhiyuan Zhou, Andy Peng, Charles Xu, Qiyang Li, Tobias Springenberg, Kevin Frans, Sergey Levine

diffusion reinforcement-learning robotics benchmarks

arXiv AI

Jun 9

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.

By Boshu Lei, Kostas Daniilidis, Antonio Loquercio

diffusion reinforcement-learning robotics fine-tuning efficiency multimodal safety

arXiv Machine Learning

Jun 8

GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios

arXiv:2606. 06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks.

By Ke Hu, Shutong Ding, Panxin Tao, Jingya Wang, Ye Shi

reinforcement-learning robotics fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 29

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.

By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta

reinforcement-learning robotics efficiency

arXiv Computer Vision

Aug 21

RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation

arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.

By Shaoxuan Wang, Guangting Zheng, Rui Huang, Zhipeng Tang, Sha Zhang, Jiajun Deng, Yanyong Zhang

diffusion reinforcement-learning robotics efficiency benchmarks

Hugging Face Trending Papers

Aug 20

RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation

Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot policies lack tractable likelihoods, limiting their use in likelihood-based offline RL post-training.

diffusion reinforcement-learning robotics efficiency benchmarks

arXiv Machine Learning

Jul 2

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning

arXiv:2603. 10263v2 Announce Type: replace-cross Abstract: We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a "distribution contraction" operator to refine pretrained generative robot policies.

By Zhanyi Sun, Shuran Song

diffusion reinforcement-learning robotics fine-tuning

arXiv AI

3d ago

Online Evolution Strategy for Flow-Matching VLA Policies via Self-Supervised Trajectory Distribution Optimization

arXiv:2609.38855v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models based on generative frameworks, such as Flow Matching, have recently achieved impressive performance in robotic m...

By Gongxin Yao, Yongsheng Zhao, Jiayin Deng, Deng Liang, Han Gao, Lei Zhao, Baoping Cheng

diffusion robotics fine-tuning efficiency multimodal

arXiv Machine Learning

Jun 2

Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies

arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.

By Hikmet Simsir, Ozgur S. Oguz

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 13

FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space

arXiv:2607. 08877v1 Announce Type: cross Abstract: Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks.

By Michael Murray, Daphne Chen, Simran Bagaria, Dean Fortier, Tess Hellebrekers, Galen Mullins, Harshavardhan Gajarla, Oier Mees, Maya Cakmak, Andrey Kolobov

diffusion reinforcement-learning robotics fine-tuning

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