arXiv Computer Vision

MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping

MultiGraspNet is a multitask 3D vision model that simultaneously predicts feasible poses for both parallel and vacuum grippers, allowing a single robot to handle multiple end effectors. Trained on the aligned GraspNet-1Billion and SuctionNet-1Billion datasets, it generates graspability masks that quantify the suitability of each scene point for successful grasps. With only 15.75 M parameters, the model achieves fast inference on a single GPU and demonstrates competitive performance against single-task models while reducing computational cost, as shown in extensive experiments and real‑world tests on a single‑arm multi‑gripper setup.

arXiv Computer Vision

4d ago

Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling

The paper introduces an interaction‑centric framework that unifies representations for two‑finger gripper manipulation across different robot embodiments. By using a parameterized universal gripper abstraction and a canonical gripper‑frame representation, the system infers sub‑tasks from language and RGB‑D inputs, grounds interaction triplets, and employs hybrid features and a Flow‑Matching Transformer to generate smooth 7‑DoF action sequences. Experiments in both simulation and real‑world settings show that this approach achieves competitive benchmark performance while enabling extreme cross‑embodiment and cross‑viewpoint zero‑shot sim‑to‑real transfer to heterogeneous robot platforms.

By Guanlin Li, Shifeng Bao, Yihan Zhao, Haitao Shen, Haoyang Li, Chen Zhao, Tong Yang, Jie Tang, Jing Zhang

llms robotics multimodal benchmarks

arXiv AI

Aug 21

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 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

Aug 21

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.

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

robotics benchmarks

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 Computer Vision

1d ago

BIND: Binding 3D Robot Actions to 2D Image Features

arXiv:2609.38443v1 Announce Type: cross Abstract: We introduce BIND, a new action representation for visuomotor robot policies that binds 3D robot actions to their corresponding 2D image features, yi...

By Cameron Smith, Arsh Tangri, Vitor Guizilini, Yue Wang, Zubair Irshad, Sergey Zakharov

robotics

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 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 Computer Vision

Sep 17

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

arXiv:2609.19142v1 Announce Type: new Abstract: World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse...

By Bardienus P. Duisterhof, Kaifeng Zhang, Adam Hung, Bowen Wen, Stan Birchfield, Yunzhu Li, Deva Ramanan, Jeffrey Ichnowski

llms robotics fine-tuning benchmarks

Hugging Face Trending Papers

Sep 16

PointZero: 3D Point Track Completion for Learning Transferable 3D Dynamics

World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on diverse volumes of data, to instill a rich prior into do...

llms robotics fine-tuning benchmarks

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

Sep 4

Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis

AdaRoboVLG is a Vision‑Language‑Grasp framework that separates a generalizable base grasp policy from task‑specific understanding. The base policy generates and evaluates physically feasible grasp candidates using kinematic mapping and force‑closure stability, while foundation‑model modules supply composable spatial, cognitive, and temporal priors that adapt grasp synthesis to different robotic hands and environments without retraining. Experiments show strong cross‑hand generalization, effective handling of diverse grasping challenges, and functional grasping in cluttered, dynamic settings.

By Sixu Yan, Shikang Wang, Binhua Huang, Xuanlai Tang, Guohua Fan, Fan Huang, Haoxuan Li, Yongkang Li, Yuhan Li, Bencheng Liao, Zeyu Zhang, Wenyu Liu, Hangxin Liu, Xinggang Wang

robotics multimodal benchmarks

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