arXiv AI August 19, 2026 By Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae

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

arXiv:2608. 17323v1 Announce Type: cross Abstract: Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performance under ideal conditions but often remain sensitive to small execution errors and distribution shifts.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 13

ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

arXiv:2512. 16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community.

By Zihan Zhou, Animesh Garg, Ajay Mandlekar, Caelan Garrett

reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

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DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration

arXiv:2608. 01452v1 Announce Type: cross Abstract: Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments.

By Haoran Liao, Pengyue Wang, Shuoyu Chen, Kehan Cheng, Xuhang Chen, Yuhao Lin, Mu Lin, Zhizhao Liang, Xiaoyi Fan, Chengyi Xing, Dan Niu, Yi-Lin Wei, Wei-Shi Zheng

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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.

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Jun 2

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.

By Taewook Nam, Junmo Cho, Youngsoo Jang, Sung Ju Hwang

reinforcement-learning robotics fine-tuning efficiency benchmarks

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

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arXiv Machine Learning

Jul 1

Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation

arXiv:2606. 31043v1 Announce Type: new Abstract: Residual reinforcement learning adapts a pretrained robot policy by learning an additive correction to its actions.

By Ethan Hirschowitz, Fabio Ramos

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Related stories

ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning

DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning

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

Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation