arXiv AI

CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning

arXiv:2608. 19776v1 Announce Type: cross Abstract: Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks.

arXiv AI

23h ago

GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation

arXiv:2608. 19759v1 Announce Type: cross Abstract: Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets.

By Julien Merand, Boris Meden, Mathieu Grossard, Liming Chen

robotics benchmarks

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

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 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 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

Jun 9

GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation

arXiv:2606. 08530v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments.

By Yuan Zhang, Shiqi Zhang, Yedong Shen, Shuai Dong, Jiajun Deng, Xin Zhang, Yuxuan Gao, Jiajia Wu, Xin Nie, Zhiyuan Cheng, Jianmin Ji, Yanyong Zhang, Xingyi Zhang, Jia Pan

robotics multimodal benchmarks

arXiv Machine Learning

Jun 29

Web2Grasp: Learning Functional Grasps from Web Images of Hand-Object Interactions

arXiv:2505. 05517v3 Announce Type: replace-cross Abstract: Functional grasping is essential for enabling dexterous multi-finger robot hands to manipulate objects effectively.

By Hongyi Chen, Yunchao Yao, Yufei Ye, Zhixuan Xu, Homanga Bharadhwaj, Jiashun Wang, Arthur Jakobsson, Ruihan Zhao, Shubham Tulsiani, Zackory Erickson, Jeffrey Ichnowski

robotics

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 19

Human Universal Grasping

arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.

By Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu, Billy Yan, Irmak Guzey, David Fouhey, Dandan Shan, Lerrel Pinto

robotics 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 Machine Learning

Jun 15

ORCA: A Platform for Open-Source Dexterity Research

arXiv:2606. 14561v1 Announce Type: cross Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation.

By Francesco Capuano, Maximilian Eberlein, Fabrice Bourquin, Clemens Claudio Christoph

agents robotics

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