arXiv Machine Learning

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.

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 Machine Learning

Jun 8

SERNF: Sample-Efficient Real-World Dexterous Policy Fine-Tuning via Action-Chunked Critics and Normalizing Flows

arXiv:2602. 09580v4 Announce Type: replace-cross Abstract: Real-world fine-tuning of dexterous manipulation policies remains challenging due to limited real-world interaction budgets and highly multimodal action distributions.

By Chenyu Yang, Denis Tarasov, Davide Liconti, Romain Guntz, Hehui Zheng, Robert K. Katzschmann

diffusion robotics fine-tuning multimodal

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 AI

Jul 14

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.

By Rushuai Yang, Zhuo Han, Houlin Li, Hecheng Wang, Zhichao Wu, Rui Zhang, Zhaowei Zhang, Zihong Chen, Xiaohan Yan, Chiming Liu, Yi Chen, Wei Shan, Maoqing Yao

reinforcement-learning robotics multimodal 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

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 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 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 2

Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry

arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.

By Zemin Yang, Yaoyu He, Yiming Zhong, Yuhao Zhang, Xinge Zhu, Yao Mu, Qingqiu Huang, Yuexin Ma

diffusion robotics efficiency

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 Machine Learning

Jul 28

Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model

arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.

By JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Where Should Action Generation Begin? A Learnable Source Prior for Generative Robot Policies

arXiv:2606. 17408v1 Announce Type: cross Abstract: Generative robot policies typically begin action generation from an observation-independent standard Gaussian distribution, leaving the choice of source distribution underexplored.

By Meipo Dai, Qiyuan Zhuang, He-Yang Xu, Ying-Jie Shuai, Yijun Wang, Qi Dou, Xiu-Shen Wei

diffusion robotics

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