arXiv AI August 19, 2026 By Amir Arsalan Nematollahi, Shayan Ahmadi, Mehdi Tale Masouleh, Ahmad Kalhor

Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision

arXiv:2608. 17628v1 Announce Type: cross Abstract: Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations.

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

arXiv Machine Learning

Jun 16

Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos

arXiv:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.

By Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu, Ali Farhadi, Shenlong Wang, Wei-Chiu Ma

robotics

arXiv AI

Jul 2

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.

By Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Huihua Zhao, John Welsh, Michael Andres Lin, Wei Liu, Tingwu Wang, Xingye Da, Zhengyi Luo, Vishal Kulkarni, Naema Bhatti, Yuke Zhu, Linxi Fan, Bowen Wen, Danfei Xu, Soha Pouya, Yan Chang

reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 10

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.

By Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin

llms agents robotics fine-tuning benchmarks

arXiv AI

Jun 18

Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation

arXiv:2606. 18594v1 Announce Type: cross Abstract: In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance.

By Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik, Colin Bellinger, A. Rupam Mahmood

reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 1

Learning Dexterous Grasping from Sparse Taxonomy Guidance

arXiv:2604. 04138v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control.

By Juhan Park, Taerim Yoon, Seungmin Kim, Joong-Gil Kim, Wontae Ye, Jeongeun Park, Yoonbyung Chai, Geonwoo Cho, Geunwoo Cho, Dohyeong Kim, Kyungjae Lee, Yong-Jae Kim, Sungjoon Choi

reinforcement-learning robotics

arXiv AI

Jul 17

Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution

arXiv:2607. 14341v1 Announce Type: cross Abstract: Robust robotic grasping remains a fundamental challenge for complex real-world applications.

By Hanyi Zhang, Khang Nguyen, Charith Munasinghe, Basu Hela, Tianyu Li, Zihong Luo, Hoan Nguyen, Hans Wernher van de Venn, Yalin Zheng, Ravi Prakash, Tung D. Ta, Anh Nguyen, Baoru Huang

robotics benchmarks

Related stories

Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation

Learning Dexterous Grasping from Sparse Taxonomy Guidance

Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution