Language-guided grasping has emerged as a promising paradigm for enabling robots to identify and manipulate target objects through natural language instructions, yet it remains highly challenging in cluttered or occluded scenes. Existing methods often rely on multi-stage pipelines that separate object perception and grasping, which leads to limited cross-modal fusion, redundant computation, and poor generalization in cluttered, occluded, or low-texture scenes. To address these limitations, we propose GeoLanG, an end-to-end multi-task framework built upon the CLIP architecture that unifies visual and linguistic inputs into a shared representation space for robust semantic alignment and improved generalization. To enhance target discrimination under occlusion and low-texture conditions, we explore a more effective use of depth information through the Depth-guided Geometric Module (DGGM), which converts depth into explicit geometric priors and injects them into the attention mechanism without additional computational overhead. In addition, we propose Adaptive Dense Channel Integration, which adaptively balances the contributions of multi-layer features to produce more discriminative and generalizable visual representations. Extensive experiments on the OCID-VLG dataset, as well as in both simulation and real-world hardware, demonstrate that GeoLanG enables precise and robust language-guided grasping in complex, cluttered environments, paving the way toward more reliable multimodal robotic manipulation in real-world human-centric settings.
MG-Grasp: Metric-Scale Geometric 6-DoF Grasping Framework with Sparse RGB Observations
The Department of Electronic Engineering, Tsinghua University, Beijing 100084, China（清华大学电子工程系，北京100084，中国）；The Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China（香港科学与技术大学电子与计算机工程系，香港，中国）
RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields
Peking University（北京大学）；Imperial College London（伦敦帝国学院）；Oxford University（牛津大学）；Tianjin University of Technology（天津理工大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
GraspView: Active Perception Scoring and Best-View Optimization for Robotic Grasping in Cluttered Environments
Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院）；Shenzhen Graduate School, Peking University（深圳研究生院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；X-Era AI Lab（X-Era AI实验室）
FunGrasp: Functional Grasping for Diverse Dexterous Hands
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；ETH Zürich（苏黎世联邦理工学院）；The Hong Kong University of Science and Technology（香港科技大学）
A Hyperspectral Imaging Guided Robotic Grasping System
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong SAR（机械与自动化工程系，T-Stone机器人研究所，香港中文大学，香港特别行政区）；School of Instrumentation and Optoelectronic Engineering, Beihang University（仪器与光电工程学院，北航大学）
GRASPLAT: Enabling dexterous grasping through novel view synthesis
TeV, Fondazione Bruno Kessler（TeV，布鲁诺·凯瑟尔基金会）；PAVIS, Fondazione Istituto Italiano di Tecnologia（PAVIS，意大利技术研究院基金会）；ISR, Instituto Superior Técnico, Universidade de Lisboa（ISR，里斯本大学理工学院）；University of Trento（特伦托大学）
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Beijing University of Posts and Telecommunications（北京邮电大学）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
Department of Electrical and Computer Engineering, Drexel University（德雷塞尔大学电气与计算机工程系）；Virginia Seafood Agricultural Research and Extension Center, and Department of Biological Systems Engineering, Virginia Tech（弗吉尼亚理工学院生物系统工程系和弗吉尼亚海鲜农业研究与推广中心）；Amazon Store Foundation AI (SFAI)（亚马逊商店基金会人工智能（SFAI））
Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Department of Artificial Intelligence, Korea University（韩国大学人工智能系）；University of California, Los Angeles（加州大学洛杉矶分校）；Department of Mechanical & Aerospace Engineering, University of California, Los Angeles（加州大学洛杉矶分校机械与航空航天工程系）；Department of Computer Science, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）
Triplane Grasping: Efficient 6-DoF Grasping with Single RGB Images
Computer Science Department, Faculty of Science and Engineering, Swansea University（计算机科学系，科学与工程学院，斯旺西大学）；Department of Computer Science, Durham University（计算机科学系，达勒姆大学）
Language-Guided Grasping under Partial Observation for Mobile Manipulation in Field Inspection and Maintenance
Department of Mechanical Engineering, Federal University of Uberlândia（联邦大学伯南迪利亚机械工程系）；Department of Mechanical Engineering, University of São Paulo（圣保罗大学机械工程系）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；Faculdade Israelita de Ensino e Pesquisa Albert Einstein, Hospital Israelita Albert Einstein（艾伯特·爱因斯坦以色列教学与研究学院，艾伯特·爱因斯坦医院）
Visual-tactile manipulation to collect household waste in outdoor
Instituto Universitario de Investigación Informática, Universidad de Alicante（阿利坎特大学大学信息学研究学院）；Universidad de Alicante（阿利坎特大学）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）
AnyViewDex: View-Invariant Dexterous Manipulation from RGB Observations
Robotics Research Center, IIIT-H（IIIT-H机器人研究中心）；Veermata Jijabai Technological Institute Mumbai（孟买维尔马塔·吉贾拜技术学院）；Indian Institute of Science Education and Research Bhopal（印度科学教育与研究学院博帕尔分校）
