Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps. Int J Robot Res. 2015;34(4–5):705–24.

Article  Google Scholar 

Redmon J, Angelova A. Real-time grasp detection using convolutional neural networks. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2015. p. 1316–22.

ten Pas A, Gualtieri M, Saenko K, Platt R. Grasp pose detection in point clouds. Int J Robot Res. 2017;36(13–14):1455–73.

Google Scholar 

Ni P, Zhang W, Bai W, Lin M, Cao Q. A new approach based on two-stream cnns for novel objects grasping in clutter. J Intell Robot Syst. 2019;94(1):161–77.

Article  Google Scholar 

Yang Y, Liang H, Choi C. A deep learning approach to grasping the invisible. IEEE Robotics and Automation Letters. 2020;5(2):2232–9.

Article  Google Scholar 

Shao Q, Hu J, Wang W, Fang Y, Liu W, Qi J, Ma J. Suction grasp region prediction using self-supervised learning for object picking in dense clutter. In: 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR). IEEE; 2019.  p. 7–12.

Bhagat S, Banerjee H, Ho Tse ZT, Ren H. Deep reinforcement learning for soft, flexible robots: Brief review with impending challenges. Robotics. 2019;8(1):4.

Article  Google Scholar 

Berscheid L, Meßner P, Kröger T. Robot learning of shifting objects for grasping in cluttered environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2019. p. 612–18.

Zeng A, Song S, Welker S, Lee J, Rodriguez A, Funkhouser T. Learning synergies between pushing and grasping with self-supervised deep reinforcement learning. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2018. p. 4238–245.

Boularias A, Bagnell JA, Stentz A. Learning to manipulate unknown objects in clutter by reinforcement. Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.

Zhang H, Lan X, Zhou X, Tian Z, Zhang Y, Zheng N. Visual manipulation relationship recognition in object-stacking scenes. Pattern Recogn Lett. 2020;140:34–42.

Article  Google Scholar 

Zuo G, Tong J, Liu H, Chen W, Li J. Graph-based visual manipulation relationship reasoning network for robotic grasping. Front Neurorobot. 2021;15.

Zhang H, Lan X, Bai S, Zhou X, Tian Z, Zheng N. Roi-based robotic grasp detection for object overlapping scenes. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2019. p. 4768–75.

Ren Y, Zhu C, Xiao S. Deformable faster E-CNN with aggregating multi-layer features for partially occluded object detection in optical remote sensing images. Remote Sens. 2018;10.9:1470.

Boroushaki T, Leng J, Clester I, Rodriguez A, Adib F. Robotic grasping of fully-occluded objects using RF perception. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2021. p. 923–29.

Cui Y, Ooga JI, Ogawa A, Matsubara T. Probabilistic active filtering with gaussian processes for occluded object search in clutter. Appl Intell. 2020;50(12):4310–24.

Article  Google Scholar 

Danielczuk M, Kurenkov A, Balakrishna A, Matl M, Wang D, Martín-Martín R, ...  Goldberg K. Mechanical search: Multi-step retrieval of a target object occluded by clutter. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE; 2019. p. 1614–621.

Danielczuk M, Angelova A, Vanhoucke V, Goldberg K. X-ray: Mechanical search for an occluded object by minimizing support of learned occupancy distributions. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2020. p. 9577–584.

Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. 2016. arXiv preprint arXiv:1609.02907.

Chen Y, Rohrbach M, Yan Z, Shuicheng Y, Feng J, Kalantidis Y. Graph-based global reasoning networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. p. 433–42.

Satorras VG, Estrach JB. Few-shot learning with graph neural networks. In: International Conference on Learning Representations. 2018.

Kampffmeyer M, Chen Y, Liang X, Wang H, Zhang Y, Xing EP. Rethinking knowledge graph propagation for zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. p. 11487–96.

Kipf TN, Welling M. Variational graph auto-encoders. 2016. arXiv preprint arXiv:1611.07308.

Jiang J, Dun C, Huang T, Lu Z. Graph convolutional reinforcement learning. In: International Conference on Learning Representations. 2019.

Zambaldi V, Raposo D, Santoro A, Bapst V, Li Y, Babuschkin I, ... Battaglia P. Relational deep reinforcement learning. 2018. arXiv preprint arXiv:1806.01830.

Li R, Jabri A, Darrell T, Agrawal P. Towards practical multi-object manipulation using relational reinforcement learning. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2020. p. 4051–58.

Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, ... Hassabis D. Human-level control through deep reinforcement learning. Nature. 2015;518(7540):529–33.

Rohmer MFE, Singh SPN. V-REP: a versatile and scalable robot simulation framework. In: IROS. 2013.

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. 2017. p. 618–26.