17.1.3.7.5 Actions, Grasping, Robot Grasping, Shape for Grasp

Chapter Contents (Back)
Grasping.
See also Hand Gesture Systems, Gesture Recognition.
See also Human Activities, Interacting with Objects.

Foresti, G.L., Pellegrino, F.A.,
Automatic Visual Recognition of Deformable Objects for Grasping and Manipulation,
SMC-C(34), No. 3, August 2004, pp. 325-333.
IEEE Abstract. 0409
BibRef

Carrasco, M.[Miguel], Clady, X.[Xavier],
Exploiting eye-hand coordination to detect grasping movements,
IVC(30), No. 11, November 2012, pp. 860-874.
Elsevier DOI 1211
Visual system; Grasping movements; Motion analysis; Hand posture; Hand gesture; Object recognition BibRef

Sreenivasa, M., Soueres, P., Laumond, J.P.,
Walking to Grasp: Modeling of Human Movements as Invariants and an Application to Humanoid Robotics,
SMC-A(42), No. 4, July 2012, pp. 880-893.
IEEE DOI 1206
BibRef

Colasanto, L., Suarez, R., Rosell, J.,
Hybrid Mapping for the Assistance of Teleoperated Grasping Tasks,
SMCS(43), No. 2, March 2013, pp. 390-401.
IEEE DOI 1303
BibRef

Zhou, K.[Kai], Richtsfeld, A.[Andreas], Varadarajan, K.M.[Karthik Mahesh], Zillich, M.[Michael], Vincze, M.[Markus],
Combining Plane Estimation with Shape Detection for Holistic Scene Understanding,
ACIVS11(736-747).
Springer DOI 1108
BibRef

Varadarajan, K.M.[Karthik Mahesh], Vincze, M.[Markus],
AfNet: The Affordance Network,
ACCV12(I:512-523).
Springer DOI 1304
BibRef
Earlier:
Knowledge Representation and Inference for Grasp Affordances,
CVS11(173-182).
Springer DOI 1109
BibRef
And:
Surface reconstruction for RGB-D data using real-time depth propagation,
Dense11(723-724).
IEEE DOI 1201
BibRef
Earlier:
Real-time depth diffusion for 3D surface reconstruction,
ICIP10(4149-4152).
IEEE DOI 1009
BibRef

Olufs, S.[Sven], Vincze, M.[Markus],
Room-structure estimation in Manhattan-like environments from dense 2˝D range data using minumum entropy and histograms,
WACV11(118-124).
IEEE DOI 1101
BibRef

Richtsfeld, M.[Mario], Vincze, M.[Markus],
Point Cloud Segmentation Based on Radial Reflection,
CAIP09(955-962).
Springer DOI 0909
BibRef

Lee, C.S.[Chan-Su], Chun, S.[Sung_Yong], Park, S.W.[Shin Won],
Tracking hand rotation and various grasping gestures from an IR camera using extended cylindrical manifold embedding,
CVIU(117), No. 12, 2013, pp. 1711-1723.
Elsevier DOI 1310
BibRef
Earlier: A1, A3, Only:
Tracking Hand Rotation and Grasping from an IR Camera Using Cylindrical Manifold Embedding,
ICPR10(2612-2615).
IEEE DOI 1008
Tracking BibRef

Song, K.T., Jiang, S.Y., Lin, M.H.,
Interactive Teleoperation of a Mobile Manipulator Using a Shared-Control Approach,
HMS(46), No. 6, December 2016, pp. 834-845.
IEEE DOI 1612
Grasping BibRef

Cotugno, G., Althoefer, K., Nanayakkara, T.,
The Role of the Thumb: Study of Finger Motion in Grasping and Reachability Space in Human and Robotic Hands,
SMCS(47), No. 7, July 2017, pp. 1061-1070.
IEEE DOI 1706
Grasping, Grippers, Kinematics, Robot kinematics, Standards, Thumb, Dexterous manipulation, grasping, humanoid robots, kinematics, multifingered, hands BibRef

Cai, M., Kitani, K.M., Sato, Y.,
An Ego-Vision System for Hand Grasp Analysis,
HMS(47), No. 4, August 2017, pp. 524-535.
IEEE DOI 1708
Cameras, Feature extraction, Image segmentation, Robots, Sensors, Taxonomy, Visualization, Egocentric vision, hand grasp, recognition, wearable, system BibRef

Song, P.[Peng], Fu, Z.Q.[Zhong-Qi], Liu, L.G.[Li-Gang],
Grasp planning via hand-object geometric fitting,
VC(34), No. 2, February 2018, pp. 257-270.
WWW Link. 1802
BibRef

Asif, U.[Umar], Bennamoun, M.[Mohammed], Sohel, F.A.[Ferdous A.],
A Multi-Modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling,
PAMI(40), No. 9, September 2018, pp. 2051-2065.
IEEE DOI 1808
BibRef
Earlier:
Model-Free Segmentation and Grasp Selection of Unknown Stacked Objects,
ECCV14(V: 659-674).
Springer DOI 1408
Labeling, Solid modeling, Proposals, Semantics, Image reconstruction, Computational modeling, semantic segmentation BibRef

González-Díaz, I.[Iván], Benois-Pineau, J.[Jenny], Domenger, J.P.[Jean-Philippe], Cattaert, D.[Daniel], de Rugy, A.[Aymar],
Perceptually-guided deep neural networks for ego-action prediction: Object grasping,
PR(88), 2019, pp. 223-235.
Elsevier DOI 1901
Human perception, Grasping action prediction, Weakly supervised active object detection BibRef

Chauhan, R., Sebastian, B., Ben-Tzvi, P.,
Grasp Prediction Toward Naturalistic Exoskeleton Glove Control,
HMS(50), No. 1, February 2020, pp. 22-31.
IEEE DOI 2001
Prediction algorithms, Exoskeletons, Principal component analysis, Sensors, Electromyography, upper limb BibRef

Irmak, E.C.[Erdem Can], Sahillioglu, Y.[Yusuf],
3D indirect shape retrieval based on hand interaction,
VC(36), No. 1, January 2020, pp. 5-17.
WWW Link. 2001
BibRef

Zhang, H.B.[Han-Bo], Lan, X.G.[Xu-Guang], Zhou, X.W.[Xin-Wen], Tian, Z.Q.[Zhi-Qiang], Zhang, Y.[Yang], Zheng, N.N.[Nan-Ning],
Visual manipulation relationship recognition in object-stacking scenes,
PRL(140), 2020, pp. 34-42.
Elsevier DOI 2012
Visual manipulation relationship, Grasp precondition, Robot vision BibRef

Zhang, H., Zhou, X., Lan, X., Li, J., Tian, Z., Zheng, N.,
A Real-Time Robotic Grasping Approach With Oriented Anchor Box,
SMCS(51), No. 5, May 2021, pp. 3014-3025.
IEEE DOI 2104
Robot kinematics, Grasping, Feature extraction, Training, Real-time systems, Prediction algorithms, Angle matching, real-time robotic grasping BibRef

Yu, Y.Y.[Ying-Ying], Cao, Z.Q.[Zhi-Qiang], Liu, Z.C.[Zhi-Cheng], Geng, W.J.[Wen-Jie], Yu, J.Z.[Jun-Zhi], Zhang, W.M.[Wei-Min],
A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping,
SMCS(52), No. 2, February 2022, pp. 1167-1181.
IEEE DOI 2201
Grasping, Robot kinematics, Manipulators, Image segmentation, Deconvolution, Machine learning, two-stream grasping convolutional neural network (CNN) BibRef

Shang, W.W.[Wei-Wei], Song, F.J.[Fang-Jing], Zhao, Z.Z.[Zeng-Zhi], Gao, H.B.[Hong-Bo], Cong, S.[Shuang], Li, Z.J.[Zhi-Jun],
Deep Learning Method for Grasping Novel Objects Using Dexterous Hands,
Cyber(52), No. 5, May 2022, pp. 2750-2762.
IEEE DOI 2206
Grasping, Indexes, Robots, Grippers, Force, Machine learning, Task analysis, Deep learning, dexterous hand, grasp posture, robotic grasp BibRef

Pattar, S.P.[Suraj Prakash], Hirakawa, T.[Tsubasa], Yamashita, T.[Takayoshi], Sawanobori, T.[Tetsuya], Fujiyoshi, H.[Hironobu],
Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data,
IEICE(E105-D), No. 9, September 2022, pp. 1600-1609.
WWW Link. 2209
BibRef

Cong, Y.[Yang], Chen, R.[Ronghan], Ma, B.[Bingtao], Liu, H.[Hongsen], Hou, D.D.[Dong-Dong], Yang, C.G.[Chen-Guang],
A Comprehensive Study of 3-D Vision-Based Robot Manipulation,
Cyber(53), No. 3, March 2023, pp. 1682-1698.
IEEE DOI 2302
Robots, Service robots, Grasping, Data acquisition, Pose estimation, Force, Cameras, 3-D object recognition, grasping estimation, robot manipulation BibRef

Jeddi, M.[Mahmoud], Khoogar, A.R.[Ahmad Reza],
A modified eye-in-hand stereo visual control for grasping unknown objects via Scara robot,
IET-IPR(17), No. 7, 2023, pp. 2015-2031.
DOI Link 2305
calibration, cameras, computer vision, decision making, estimation theory, feedback, image matching, Kalman filters, motion estimation BibRef

Ren, G.L.[Guang-Li], Geng, W.J.[Wen-Jie], Guan, P.Y.[Pei-Yu], Cao, Z.Q.[Zhi-Qiang], Yu, J.Z.[Jun-Zhi],
Pixel-Wise Grasp Detection via Twin Deconvolution and Multi-Dimensional Attention,
CirSysVideo(33), No. 8, August 2023, pp. 4002-4010.
IEEE DOI 2308
Feature extraction, Convolution, Deconvolution, Decoding, Robots, Kernel, Correlation, Grasp detection, multi-dimensional attention, twin deconvolution BibRef

Zhu, T.Q.[Tian-Qiang], Wu, R.[Rina], Hang, J.[Jinglue], Lin, X.B.[Xiang-Bo], Sun, Y.[Yi],
Toward Human-Like Grasp: Functional Grasp by Dexterous Robotic Hand Via Object-Hand Semantic Representation,
PAMI(45), No. 10, October 2023, pp. 12521-12534.
IEEE DOI 2310
BibRef
Earlier: A1, A2, A4, A5, Only:
Toward Human-Like Grasp: Dexterous Grasping via Semantic Representation of Object-Hand,
ICCV21(15721-15731)
IEEE DOI 2203
Codes, Annotations, Computational modeling, Semantics, Grasping, Data models, Vision for robotics and autonomous vehicles, BibRef

Ren, S.Y.[Shu-Yang], Zhang, Y.B.[Yi-Biao], Hang, J.[Jinglue], Lin, X.B.[Xiang-Bo],
Hand-object information embedded dexterous grasping generation,
PRL(174), 2023, pp. 130-136.
Elsevier DOI 2310
Dexterous multi-fingered hands, Grasp generation, Computer vision BibRef


Wan, W.K.[Wei-Kang], Geng, H.R.[Hao-Ran], Liu, Y.[Yun], Shan, Z.K.[Zi-Kang], Yang, Y.D.[Yao-Dong], Yi, L.[Li], Wang, H.[He],
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning,
ICCV23(3868-3879)
IEEE DOI 2401
BibRef

Liu, S.W.[Shao-Wei], Zhou, Y.[Yang], Yang, J.[Jimei], Gupta, S.[Saurabh], Wang, S.[Shenlong],
ContactGen: Generative Contact Modeling for Grasp Generation,
ICCV23(20552-20563)
IEEE DOI 2401
BibRef

Zheng, Y.Z.[Yan-Zhao], Shi, Y.Z.[Yun-Zhou], Cui, Y.H.[Yu-Hao], Zhao, Z.Z.[Zhong-Zhou], Luo, Z.[Zhiling], Zhou, W.[Wei],
COOP: Decoupling and Coupling of Whole-Body Grasping Pose Generation,
ICCV23(2163-2173)
IEEE DOI Code:
WWW Link. 2401
BibRef

Xu, W.Q.[Wen-Qiang], Yu, Z.J.[Zhen-Jun], Xue, H.[Han], Ye, R.L.[Ruo-Lin], Yao, S.[Siqiong], Lu, C.W.[Ce-Wu],
Visual-Tactile Sensing for In-Hand Object Reconstruction,
CVPR23(8803-8812)
IEEE DOI 2309
BibRef

Tendulkar, P.[Purva], Surís, D.[Dídac], Vondrick, C.[Carl],
FLEX: Full-Body Grasping Without Full-Body Grasps,
CVPR23(21179-21189)
IEEE DOI 2309
BibRef

Liu, J.[Jirong], Zhang, R.[Ruo], Fang, H.S.[Hao-Shu], Gou, M.H.[Ming-Hao], Fang, H.J.[Hong-Jie], Wang, C.X.[Chen-Xi], Xu, S.[Sheng], Yan, H.X.[Heng-Xu], Lu, C.W.[Ce-Wu],
Target-referenced Reactive Grasping for Dynamic Objects,
CVPR23(8824-8833)
IEEE DOI 2309
BibRef

Xu, Y.Z.[Yin-Zhen], Wan, W.K.[Wei-Kang], Zhang, J.L.[Jia-Liang], Liu, H.R.[Hao-Ran], Shan, Z.[Zikang], Shen, H.[Hao], Wang, R.C.[Rui-Cheng], Geng, H.R.[Hao-Ran], Weng, Y.J.[Yi-Jia], Chen, J.Y.[Jia-Yi], Liu, T.Y.[Teng-Yu], Yi, L.[Li], Wang, H.[He],
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy,
CVPR23(4737-4746)
IEEE DOI 2309
BibRef

Wong, A.[Alexander], Wu, Y.F.[Yi-Fan], Abbasi, S.[Saad], Nair, S.[Saeejith], Chen, Y.H.[Yu-Hao], Shafiee, M.J.[Mohammad Javad],
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge,
NAS23(2293-2297)
IEEE DOI 2309
BibRef

Zhong, J.Y.[Jing-Yu], Yuan, X.G.[Xin-Guang], Du, B.[Bo], Hu, G.[Gang], Zhao, C.Y.[Cong-Yao],
An Lévy Flight Based Honey Badger Algorithm for Robot Gripper Problem,
ICIVC22(901-905)
IEEE DOI 2301
Force, Metaheuristics, Behavioral sciences, Grippers, Optimization, Robots, Faces, component, honey badger algorithm, lévy flight, robot gripper problem BibRef

Chumbley, L.[Lachlan], Gu, M.[Morris], Newbury, R.[Rhys], Leitner, J.[Jürgen], Cosgun, A.[Akansel],
Integrating High-Resolution Tactile Sensing into Grasp Stability Prediction,
CRV22(98-105)
IEEE DOI 2301
Neural networks, Tactile sensors, Grasping, Data collection, Benchmark testing, Robot sensing systems, Stability analysis, Sensor Fusion BibRef

Gohil, P.[Priteshkumar], Thoduka, S.[Santosh], Plöger, P.G.[Paul G.],
Sensor Fusion and Multimodal Learning for Robotic Grasp Verification Using Neural Networks,
ICPR22(5111-5117)
IEEE DOI 2212
Visualization, Fuses, Neural networks, Sensor fusion, Robot sensing systems, Minimization BibRef

Wu, Y.[Yan], Wang, J.H.[Jia-Hao], Zhang, Y.[Yan], Zhang, S.W.[Si-Wei], Hilliges, O.[Otmar], Yu, F.[Fisher], Tang, S.[Siyu],
SAGA: Stochastic Whole-Body Grasping with Contact,
ECCV22(VI:257-274).
Springer DOI 2211
BibRef

Wang, K.[Kai], Guerrero, P.[Paul], Kim, V.G.[Vladimir G.], Chaudhuri, S.[Siddhartha], Sung, M.[Minhyuk], Ritchie, D.[Daniel],
The Shape Part Slot Machine: Contact-Based Reasoning for Generating 3D Shapes from Parts,
ECCV22(III:610-626).
Springer DOI 2211
BibRef

Turpin, D.[Dylan], Wang, L.Q.[Li-Quan], Heiden, E.[Eric], Chen, Y.C.[Yun-Chun], Macklin, M.[Miles], Tsogkas, S.[Stavros], Dickinson, S.[Sven], Garg, A.[Animesh],
Grasp'D: Differentiable Contact-Rich Grasp Synthesis for Multi-Fingered Hands,
ECCV22(VI:201-221).
Springer DOI 2211
BibRef

Wen, H.T.[Hong-Tao], Yan, J.H.[Jian-Hang], Peng, W.L.[Wan-Li], Sun, Y.[Yi],
TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance,
ECCV22(XXIX:445-461).
Springer DOI 2211
BibRef

Tse, T.H.E.[Tze Ho Elden], Kim, K.I.[Kwang In], Leonardis, A.[Aleš], Chang, H.J.[Hyung Jin],
Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution,
CVPR22(1654-1664)
IEEE DOI 2210
Training, Representation learning, Solid modeling, Shape, Convolution, Pose estimation, 3D from single images, Representation learning BibRef

Yang, L.X.[Li-Xin], Li, K.[Kailin], Zhan, X.Y.[Xin-Yu], Lv, J.[Jun], Xu, W.Q.[Wen-Qiang], Li, J.F.[Jie-Feng], Lu, C.W.[Ce-Wu],
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis,
CVPR22(2740-2750)
IEEE DOI 2210
Training, Codes, Computational modeling, Pose estimation, Pipelines, 3D from single images, Pose estimation and tracking BibRef

Taheri, O.[Omid], Choutas, V.[Vasileios], Black, M.J.[Michael J.], Tzionas, D.[Dimitrios],
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping,
CVPR22(13253-13263)
IEEE DOI 2210
Solid modeling, Codes, Tracking, Shape, Avatars, Pose estimation and tracking, Motion and tracking BibRef

Christen, S.[Sammy], Kocabas, M.[Muhammed], Aksan, E.[Emre], Hwangbo, J.[Jemin], Song, J.[Jie], Hilliges, O.[Otmar],
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions,
CVPR22(20545-20554)
IEEE DOI 2210
Codes, Dynamics, Reinforcement learning, Grasping, Cognition, Pattern recognition, Face and gestures, Others BibRef

Jiang, H.W.[Han-Wen], Liu, S.W.[Shao-Wei], Wang, J.S.[Jia-Shun], Wang, X.L.[Xiao-Long],
Hand-Object Contact Consistency Reasoning for Human Grasps Generation,
ICCV21(11087-11096)
IEEE DOI 2203
Training, Cognition, Task analysis, Grippers, Gestures and body pose, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Landgraf, Z.[Zoe], Scona, R.[Raluca], Laidlow, T.[Tristan], James, S.[Stephen], Leutenegger, S.[Stefan], Davison, A.J.[Andrew J.],
SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks,
ICCV21(12992-13002)
IEEE DOI 2203
Solid modeling, Shape, Grasping, Predictive models, Probabilistic logic, 3D from a single image and shape-from-x, Vision for robotics and autonomous vehicles BibRef

Wang, C.X.[Chen-Xi], Fang, H.S.[Hao-Shu], Gou, M.H.[Ming-Hao], Fang, H.J.[Hong-Jie], Gao, J.[Jin], Lu, C.W.[Ce-Wu],
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection,
ICCV21(15944-15953)
IEEE DOI 2203
Geometry, Filtering, Computational modeling, Current measurement, Neural networks, Grasping, BibRef

Wu, Y.M.[Yan-Min], Zhang, Y.Z.[Yun-Zhou], Zhu, D.L.[De-Long], Chen, X.[Xin], Coleman, S.[Sonya], Sun, W.K.[Wen-Kai], Hu, X.G.[Xing-Gang], Deng, Z.Q.[Zhi-Qiang],
Object SLAM-Based Active Mapping and Robotic Grasping,
3DV21(1372-1381)
IEEE DOI 2201
Solid modeling, Simultaneous localization and mapping, Uncertainty, Shape, Pose estimation, Grasping, Object SLAM, Robotic Grasping BibRef

Yin, Y.[Yue], McCarthy, C.[Chris], Rezazadegan, D.[Dana],
Real-Time 3D Hand-Object Pose Estimation for Mobile Devices,
ICIP21(3288-3292)
IEEE DOI 2201
Performance evaluation, Solid modeling, Image recognition, Computational modeling, Pose estimation, Memory management, augmented reality BibRef

Cheng, Z.[Zida], Chen, S.[Siheng], Zhang, Y.[Ya],
Semi-Supervised 3D Hand-Object Pose Estimation Via Pose Dictionary Learning,
ICIP21(3632-3636)
IEEE DOI 2201
Estimation error, Pose estimation, Machine learning, Data collection, Cameras, Hand-object pose estimation, pose dictionary learning BibRef

Xu, S.X.[Si-Xiong], Gong, P.[Pei], Dong, Y.C.[Yan-Chao], Gi, L.L.[Ling-Ling], Huang, C.[Cheng], Wang, S.B.[Si-Biao],
Pose Estimation of Texture-Less Targets for Unconstrained Grasping,
ISVC21(I:466-477).
Springer DOI 2112
BibRef

Grady, P.[Patrick], Tang, C.C.[Cheng-Cheng], Twigg, C.D.[Christopher D.], Vo, M.[Minh], Brahmbhatt, S.[Samarth], Kemp, C.C.[Charles C.],
ContactOpt: Optimizing Contact to Improve Grasps,
CVPR21(1471-1481)
IEEE DOI 2111
Deformable models, Codes, Pose estimation, Biological tissues, Kinematics, Data models BibRef

Liu, H.B.[Hong-Bin], Jia, J.Y.[Jin-Yuan], Gong, N.Z.Q.[Neil Zhen-Qiang],
PointGuard: Provably Robust 3D Point Cloud Classification,
CVPR21(6182-6191)
IEEE DOI 2111
Smoothing methods, Computational modeling, Grasping, Benchmark testing, Prediction algorithms BibRef

Prew, W.[William], Breckon, T.[Toby], Bordewich, M.[Magnus], Beierholm, U.[Ulrik],
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss,
ICPR21(9843-9850)
IEEE DOI 2105
Training, Image color analysis, Grasping, Real-time systems, Pattern recognition, Task analysis, Grippers BibRef

de Gregorio, D.[Daniele], Zanella, R.[Riccardo], Palli, G.[Gianluca], di Stefano, L.[Luigi],
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings,
ICPR21(7419-7426)
IEEE DOI 2105
Deep learning, Training, Automation, Service robots, Pose estimation, Training data, Grasping BibRef

Arora, P.[Prateek], Papachristos, C.[Christos],
Mobile Manipulator Robot Visual Servoing and Guidance for Dynamic Target Grasping,
ISVC20(II:223-235).
Springer DOI 2103
BibRef

Karunratanakul, K., Tang, S.[Siyu], Zhang, Y., Black, M.J., Muandet, K.[Krikamol], Tang, S.,
Grasping Field: Learning Implicit Representations for Human Grasps,
3DV20(333-344)
IEEE DOI 2102
Grasping, Solid modeling, Image reconstruction, Neural networks, Task analysis, Data models, grasp generation BibRef

Chao, Y.W.[Yu-Wei], Yang, W.[Wei], Xiang, Y.[Yu], Molchanov, P.[Pavlo], Handa, A.[Ankur], Tremblay, J.[Jonathan], Narang, Y.S.[Yashraj S.], van Wyk, K.[Karl], Iqbal, U.[Umar], Birchfield, S.[Stan], Kautz, J.[Jan], Fox, D.[Dieter],
DexYCB: A Benchmark for Capturing Hand Grasping of Objects,
CVPR21(9040-9049)
IEEE DOI 2111
Pose estimation, Grasping, Benchmark testing, Handover, Drives BibRef

Ehsani, K.[Kiana], Han, W.[Winson], Herrasti, A.[Alvaro], VanderBilt, E.[Eli], Weihs, L.[Luca], Kolve, E.[Eric], Kembhavi, A.[Aniruddha], Mottaghi, R.[Roozbeh],
ManipulaTHOR: A Framework for Visual Object Manipulation,
CVPR21(4495-4504)
IEEE DOI 2111
Visualization, Navigation, Mobile agents, Grasping, Manipulators, Planning BibRef

Taheri, O.[Omid], Ghorbani, N.[Nima], Black, M.J.[Michael J.], Tzionas, D.[Dimitrios],
Grab: A Dataset of Whole-body Human Grasping of Objects,
ECCV20(IV:581-600).
Springer DOI 2011
BibRef

Gong, S., Bahri, M., Bronstein, M.M., Zafeiriou, S.P.[Stefanos P.],
Geometrically Principled Connections in Graph Neural Networks,
CVPR20(11412-11421)
IEEE DOI 2008
Convolution, Neural networks, Machine learning, Kernel, Interpolation, Task analysis, Shape BibRef

Fang, H., Wang, C., Gou, M., Lu, C.,
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping,
CVPR20(11441-11450)
IEEE DOI 2008
Grasping, Cameras, Robot vision systems, Benchmark testing, Machine learning, Robustness BibRef

Shan, D., Geng, J., Shu, M., Fouhey, D.F.,
Understanding Human Hands in Contact at Internet Scale,
CVPR20(9866-9875)
IEEE DOI 2008
Videos, Internet, Image reconstruction, YouTube, Data mining BibRef

James, S.[Stephen], Wohlhart, P.[Paul], Kalakrishnan, M.[Mrinal], Kalashnikov, D.[Dmitry], Irpan, A.[Alex], Ibarz, J.[Julian], Levine, S.[Sergey], Hadsell, R.[Raia], Bousmalis, K.[Konstantinos],
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks,
CVPR19(12619-12629).
IEEE DOI 2002
BibRef

Brahmbhatt, S.[Samarth], Ham, C.[Cusuh], Kemp, C.C.[Charles C.], Hays, J.[James],
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging,
CVPR19(8701-8711).
IEEE DOI 2002
BibRef

Tang, Q.[Qirong], Hu, X.[Xue], Chu, Z.[Zhugang], Wu, S.[Shun],
6d Gripper Pose Estimation from RGB-D Image,
CVS19(120-125).
Springer DOI 1912
BibRef

Kiatos, M.[Marios], Malassiotis, S.[Sotiris],
Grasping Unknown Objects by Exploiting Complementarity with Robot Hand Geometry,
CVS19(88-97).
Springer DOI 1912
BibRef

Gritsenko, P.[Pavel], Gritsenko, I.[Igor], Seidakhmet, A.[Askar], Kwolek, B.[Bogdan],
Plane-Based Humanoid Robot Navigation and Object Model Construction for Grasping,
4DPose18(I:649-664).
Springer DOI 1905
BibRef

Ghazaei, G.[Ghazal], Laina, I.[Iro], Rupprecht, C.[Christian], Tombari, F.[Federico], Navab, N.[Nassir], Nazarpour, K.[Kianoush],
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions,
ACCV18(IV:38-55).
Springer DOI 1906
BibRef

Gupta, K., Burschka, D., Bhavsar, A.,
Effectiveness of Grasp Attributes and Motion-Constraints for Fine-Grained Recognition of Object Manipulation Actions,
Hands16(1232-1239)
IEEE DOI 1612
BibRef

de Gregorio, D.[Daniele], Tombari, F.[Federico], di Stefano, L.[Luigi],
RobotFusion: Grasping with a Robotic Manipulator via Multi-view Reconstruction,
6DPose16(III: 634-647).
Springer DOI 1611
BibRef

Lakani, S.R.[Safoura Rezapour], Rodríguez-Sánchez, A.J.[Antonio J.], Piater, J.H.[Justus H.],
Can Affordances Guide Object Decomposition into Semantically Meaningful Parts?,
WACV17(82-90)
IEEE DOI 1609
Grasping, Histograms, Semantics, Shape, Visualization BibRef

Yang, Y.Z.[Ye-Zhou], Fermuller, C.[Cornelia], Li, Y.[Yi], Aloimonos, Y.F.[Yi-Fannis],
Grasp type revisited: A modern perspective on a classical feature for vision,
CVPR15(400-408)
IEEE DOI 1510
BibRef

Huang, D.A.[De-An], Ma, M.H.[Ming-Huang], Ma, W.C.[Wei-Chiu], Kitani, K.M.[Kris M.],
How do we use our hands? Discovering a diverse set of common grasps,
CVPR15(666-675)
IEEE DOI 1510
BibRef

Lakani, S.R.[Safoura Rezapour], Popa, M.[Mirela], Rodriguez-Sanchez, A.J.[Antonio J.], Piater, J.H.[Justus H.],
CPS: 3D Compositional Part Segmentation through Grasping,
CRV15(117-124)
IEEE DOI 1507
Feature extraction BibRef

Kim, J.W., You, S., Ji, S.H., Kim, H.S.,
Real-Time Hand Grasp Recognition Using Weakly Supervised Two-Stage Convolutional Neural Networks for Understanding Manipulation Actions,
DeepLearnRV17(481-483)
IEEE DOI 1709
Feature extraction, Grasping, Image recognition, Network architecture, Pattern recognition, Real-time, systems BibRef

Thonnagith, P., Sudsang, A.,
New hand posture classification strategy for finding kinematically-feasible precision grasps,
ICARCV10(1583-1588).
IEEE DOI 1109
BibRef

Wörsdörfer, F.[Florian], Stock, F.[Florian], Bayro-Corrochano, E.[Eduardo], Hildenbrand, D.[Dietmar],
Optimizations and Performance of a Robotics Grasping Algorithm Described in Geometric Algebra,
CIARP09(263-271).
Springer DOI 0911
BibRef

Yasumuro, Y.[Yoshihiro], Yamazaki, M.[Masayuki], Imura, M.[Masataka], Manabe, Y.[Yoshitsugu], Chihara, K.[Kunihiro],
Grasp Motion Synthesis Based on Object Features,
AMDO06(305-314).
Springer DOI 0607
BibRef

Taylor, M.J., Blake, A.,
Grasping the Apparent Contour,
ECCV94(B:25-34).
Springer DOI BibRef 9400

Blake, A., Taylor, M.J., and Cox, A.,
Grasping Visual Symmetry,
ICCV93(724-733).
IEEE DOI Symmetry as the locus of bi-tangent circles. BibRef 9300

Gatrell, L.B.[Lance B.],
CAD-Based Grasp Synthesis Utilizing Polygons, Edges, and Vertices,
CRA89(184-189). BibRef 8900

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Carried Objects, Carrying Objects .


Last update:Feb 29, 2024 at 09:13:14