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In recent years, there has been a significant effort dedicated to developing efficient, robust, and general human-to-robot handover systems.
K. Nagata, Y. Oosaki, M. Kakikura, and H. Tsukune, “Delivery by hand between human and robot based on fingertip force-torque information,” in
1998
Earlier work this paper cites.
S. Kajikawa and E. Ishikawa, “Trajectory planning for hand-over between human and robot,” in
2000
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A. Edsinger and C. C. Kemp, “Human-robot interaction for cooperative manipulation: Handing objects to one another,” in
2007
Earlier work this paper cites.
T. Kröger and F. M. Wahl, “Online trajectory generation: Basic concepts for instantaneous reactions to unforeseen events,”
2009
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in
2011
Earlier work this paper cites.
J. Aleotti, V. Micelli, and S. Caselli, “Comfortable robot to human object hand-over,” in
2012
Earlier work this paper cites.
A. Aitor, Z. Marton, F. Tombari, W. Wohlkinger, C. Potthast, B. Zeisl, R. Rusu, S. Gedikli, and M. Vincze, “Point cloud library,”
2012
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K. Strabala, M. K. Lee, A. Dragan, J. Forlizzi, S. S. Srinivasa, M. Cakmak, and V. Micelli, “Toward seamless human-robot handovers,”
2013
Earlier work this paper cites.
E. C. Grigore, K. Eder, A. G. Pipe, C. Melhuish, and U. Leonards, “Joint action understanding improves robot-to-human object handover,” in
2013
Earlier work this paper cites.
M. Prada, A. Remazeilles, A. Koene, and S. Endo, “Implementation and experimental validation of dynamic movement primitives for object handover,” in
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,”
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
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2016
Earlier work this paper cites.
J. Konstantinova, S. Krivic, A. Stilli, J. Piater, and K. Althoefer, “Autonomous object handover using wrist tactile information,” in
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”
2017
Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,”
2017
Cited alongside, same era.
W. Wang, R. Li, Z. M. Diekel, Y. Chen, Z. Zhang, and Y. Jia, “Controlling object hand-over in human–robot collaboration via natural wearable sensing,”
2018
Cited alongside, same era.
D. Vogt, S. Stepputtis, B. Jung, and H. B. Amor, “One-shot learning of human–robot handovers with triadic interaction meshes,”
2018
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in
2020
Later among the works it cites.
V. Ortenzi, A. Cosgun, T. Pardi, W. P. Chan, E. Croft, and D. Kuli’c, “Object handovers: a review for robotics,”
2021
Later among the works it cites.
W. Yang, C. Paxton, A. Mousavian, Y.-W. Chao, M. Cakmak, and D. Fox, “Reactive human-to-robot handovers of arbitrary objects,” in
2021
Later among the works it cites.
C. Wang, H.-S. Fang, M. Gou, H. Fang, J. Gao, and C. Lu, “Graspness discovery in clutters for fast and accurate grasp detection,” in
2021
Later among the works it cites.
M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in
2021
Later among the works it cites.
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Cited alongside, same era.
M. Bianchi, G. Averta, E. Battaglia, C. Rosales, M. Bonilla, A. Tondo, M. Poggiani, G. Santaera, S. Ciotti, M. G. Catalano,
2018
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,”
2018
Cited alongside, same era.
D. Morrison, J. Leitner, and P. Corke, “Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach,” in
2018
Cited alongside, same era.
N. Marturi, M. Kopicki, A. Rastegarpanah, V. Rajasekaran, M. Adjigble, R. Stolkin, A. Leonardis, and Y. Bekiroglu, “Dynamic grasp and trajectory planning for moving objects,”
2019
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in
2019
Cited alongside, same era.
C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in
2019
Cited alongside, same era.
P. Rosenberger, A. Cosgun, R. Newbury, J. Kwan, V. Ortenzi, P. Corke, and M. Grafinger, “Object-independent human-to-robot handovers using real time robotic vision,”
2020
Cited alongside, same era.
M. Mavsar and A. Ude, “Rovernet: Vision-based adaptive human-to-robot object handovers,” in
2022
Later among the works it cites.
W. Yang, B. Sundaralingam, C. Paxton, I. Akinola, Y.-W. Chao, M. Cakmak, and D. Fox, “Model predictive control for fluid human-to-robot handovers,” in
2022
Later among the works it cites.
L. Wang, Y. Xiang, W. Yang, A. Mousavian, and D. Fox, “Goal-auxiliary actor-critic for 6d robotic grasping with point clouds,” in
2022
Later among the works it cites.
X. Zhou, T. Yin, V. Koltun, and P. Krähenbühl, “Global tracking transformers,” in
2022
Later among the works it cites.
A. Mazhitov, T. Syrymova, Z. Kappassov, and M. Rubagotti, “Human–robot handover with prior-to-pass soft/rigid object classification via tactile glove,”
2023
Closest in time.
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,”
2023
Closest in time.
J. Liu, R. Zhang, H.-S. Fang, M. Gou, H. Fang, C. Wang, S. Xu, H. Yan, and C. Lu, “Target-referenced reactive grasping for dynamic objects,” in
2023
Closest in time.