arXiv AI

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.

arXiv AI

Jun 26

Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?

arXiv:2606. 26428v1 Announce Type: cross Abstract: Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach.

By Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu, Jeannette Bohg

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 11

Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

arXiv:2606. 12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers.

By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding

diffusion robotics fine-tuning multimodal 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

Jul 29

InDex: Empowering VLA Models with Intent-Conditioned Arm-Hand Coordination for Dexterous Manipulation

arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.

By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding

diffusion robotics multimodal

arXiv Machine Learning

Jul 15

Robust In-Hand Manipulation via Priors in Reinforcement Learning and Mechanical Design

arXiv:2607. 12105v1 Announce Type: cross Abstract: In-hand manipulation without external sensing is challenging due to uncertainties from finger-object contacts and disturbances by gravity.

By Yifei Chen, Shihan Lu, Ed Colgate, Kevin Lynch

reinforcement-learning robotics

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

Jun 12

Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning

arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.

By Allison Andreyev, Landon Eum, Nestor Tiglao, Romel Gomez

llms robotics fine-tuning multimodal benchmarks

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

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 AI

22h ago

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.

By Amir Arsalan Nematollahi, Shayan Ahmadi, Mehdi Tale Masouleh, Ahmad Kalhor

reinforcement-learning robotics

arXiv AI

Jun 29

DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand

arXiv:2606. 28323v1 Announce Type: cross Abstract: Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging.

By Dihong Huang, Zhenyu Wei, Zhuxiu Xu, Yunchao Yao, Sikai Li, Mingyu Ding

robotics

Hugging Face Trending Papers

Jun 11

Mana: Dexterous Manipulation of Articulated Tools

Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies.

agents reinforcement-learning robotics

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