State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To improve grasping performance on such objects, we introduce a method for learning a multi-modal perception model by bootstrapping from an existing uni-modal model. This transfer learning approach requires only a pre-existing uni-modal grasping model and paired multi-modal image data for training, foregoing the need for ground-truth grasp success labels nor real grasp attempts. Our experiments demonstrate that our approach is able to reliably grasp transparent and reflective objects. Video and supplementary material are available at https://sites.google.com/view/transparent-specular-grasping.
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）
Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Institute of Artificial Intelligence, Collaborative Innovation Center for the Marine Artificial Intelligence, Shanghai University（上海大学人工智能学院、海洋人工智能协同创新中心）
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects
College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization（工业智能与系统优化国家前沿科学中心）
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）
Trans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality
Shanghai AI Laboratory（上海人工智能实验室）；SUSTech（南方科技大学）；CUHK（香港中文大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Oxford（牛津大学）；Beihang University（北京航空航天大学）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）
ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera
Samsung R&D Institute China-Beijing（三星中国北京研发中心）；CFCS, School of Computer Science, Peking University（计算机科学系，北京大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Qiyuan Lab（启元实验室）
TransGraspNet: Physically and Geometrically Consistent Manipulation of Transparent Labware
Peking University（北京大学）；School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；Shanghai Jiao Tong University（上海交通大学）；Southern University of Science and Technology（南方科技大学）
Real-Time Glass Detection and Reprojection using Sensor Fusion Onboard Aerial Robots
ARL DCIST CRA；NSF Grant CCR-2112665（NSF CCR-2112665）；NSF Graduate Research Fellowship Grant No. DGE-1845298（NSF DGE-1845298）；IoT4Ag Engineering Research Center
AISPO: Enhancing Depth Reliability for Robotic Manipulation of Non-Lambertian Objects via Affine-Invariant Shape Prior
The Hong Kong University of Science & Technology（香港科技大学）；The University of Hong Kong（香港大学）；Southern University of Science & Technology（南方科技大学）；Zhejiang University（浙江大学）；LimX Dynamics
