arXiv Machine Learning June 11, 2026 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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

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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.

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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

Jul 31

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.

By Zhaoyuan Gu, Yipu Chen, Zimeng Chai, Alfred Cueva, Thong Nguyen, Yifan Wu, Huishu Xue, Minji Kim, Isaac Legene, Fukang Liu, KyoungMok Kim, Ayan Barula, Yongxin Chen, Ye Zhao

diffusion reinforcement-learning robotics fine-tuning

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 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 AI

Aug 19

ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback

The paper introduces ORPA, a framework that adds a lightweight, feedback-conditioned module to a pretrained robotic manipulation policy, enabling real‑time residual adjustments in joint space without retraining the base policy. ORPA allows immediate correction of execution errors and distribution shifts, improving success rates and recovery on precision‑sensitive tasks compared to baseline policies and rule‑based inverse kinematics. The method is evaluated on the ALOHA platform, showing its effectiveness in real‑time deployment scenarios.

By Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae

llms rag robotics

arXiv AI

Aug 13

TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning

arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.

By Matthew M. Hong, Jesse Zhang, Anusha Nagabandi, Abhishek Gupta

agents diffusion reinforcement-learning robotics fine-tuning

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

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

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

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

ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback

TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning