{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:14:23Z","timestamp":1728177263793},"reference-count":137,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"Deanship of Scientific Research at King Khalid University","doi-asserted-by":"publisher","award":["RGP.2\/49\/43"],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012024","name":"Multimedia University","doi-asserted-by":"publisher","award":["MMUI\/190004.02"],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.<\/jats:p>","DOI":"10.3390\/s22207938","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T04:58:51Z","timestamp":1666155531000},"page":"7938","source":"Crossref","is-referenced-by-count":16,"title":["Review of Learning-Based Robotic Manipulation in Cluttered Environments"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9734-6267","authenticated-orcid":false,"given":"Marwan Qaid","family":"Mohammed","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University (MMU), Ayer Keroh, Melaka 75450, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1242-5084","authenticated-orcid":false,"given":"Lee Chung","family":"Kwek","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University (MMU), Ayer Keroh, Melaka 75450, Malaysia"}]},{"given":"Shing Chyi","family":"Chua","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University (MMU), Ayer Keroh, Melaka 75450, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0729-2654","authenticated-orcid":false,"given":"Arafat","family":"Al-Dhaqm","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0360-5270","authenticated-orcid":false,"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems, Research and Innovation, (IISRI), Deakin University, Geelong, VIC 3216, Australia"}]},{"given":"Taiseer Abdalla Elfadil","family":"Eisa","sequence":"additional","affiliation":[{"name":"Department of Information Systems-Girls Section, King Khalid University, Mahayil 62529, Saudi Arabia"}]},{"given":"Muhammad Fahmi","family":"Miskon","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka 76100, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8802-487X","authenticated-orcid":false,"given":"Mohammed Nasser","family":"Al-Mhiqani","sequence":"additional","affiliation":[{"name":"Faculty of Information Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka 76100, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5542-5952","authenticated-orcid":false,"given":"Abdulalem","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8546-9470","authenticated-orcid":false,"given":"Mohammed","family":"Abaker","sequence":"additional","affiliation":[{"name":"Department Computer Science of Community College, King Khalid University, Muhayel Aseer 61913, Saudi Arabia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1132-6095","authenticated-orcid":false,"given":"Esmail Ali","family":"Alandoli","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Multimedia University (MMU), Ayer Keroh, Melaka 75450, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.rcim.2014.04.005","article-title":"Object recognition and pose estimation for industrial applications: A cascade system","volume":"30","author":"Rocha","year":"2014","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1017\/S0263574721000023","article-title":"Comprehensive Review on Reaching and Grasping of Objects in Robotics","volume":"39","author":"Marwan","year":"2021","journal-title":"Robotica"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.robot.2015.07.015","article-title":"Tactile sensing in dexterous robot hands\u2014Review","volume":"74","author":"Kappassov","year":"2015","journal-title":"Rob. Auton. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/ACCESS.2015.2482543","article-title":"Sensors for robotic hands: A survey of state of the art","volume":"3","author":"Saudabayev","year":"2015","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.mechatronics.2017.11.002","article-title":"Robotic tactile perception of object properties: A review","volume":"48","author":"Luo","year":"2017","journal-title":"Mechatronics"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zou, L., Ge, C., Wang, Z.J., Cretu, E., and Li, X. (2017). Novel tactile sensor technology and smart tactile sensing systems: A review. Sensors, 17.","DOI":"10.3390\/s17112653"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chi, C., Sun, X., Xue, N., Li, T., and Liu, C. (2018). Recent progress in technologies for tactile sensors. Sensors, 18.","DOI":"10.3390\/s18040948"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.robot.2016.10.003","article-title":"Finger design automation for industrial robot grippers: A review","volume":"87","author":"Honarpardaz","year":"2017","journal-title":"Rob. Auton. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frobt.2016.00069","article-title":"Soft manipulators and grippers: A review","volume":"3","author":"Hughes","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1707035","DOI":"10.1002\/adma.201707035","article-title":"Soft Robotic Grippers","volume":"30","author":"Shintake","year":"2018","journal-title":"Adv. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Terrile, S., Arg\u00fcelles, M., and Barrientos, A. (2021). Comparison of different technologies for soft robotics grippers. Sensors, 21.","DOI":"10.3390\/s21093253"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S1672-6529(16)60293-7","article-title":"Bioinspired Dry Adhesive Materials and Their Application in Robotics: A Review","volume":"13","author":"Li","year":"2016","journal-title":"J. Bionic Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/adma.201604977","article-title":"Elastic Inflatable Actuators for Soft Robotic Applications","volume":"29","author":"Gorissen","year":"2017","journal-title":"Adv. Mater."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MRA.2016.2616538","article-title":"Cognition-Enabled Robot Manipulation in Human Environments: Requirements, Recent Work, and Open Problems","volume":"24","author":"Ersen","year":"2017","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"eaat8414","DOI":"10.1126\/science.aat8414","article-title":"Trends and challenges in robot manipulation","volume":"364","author":"Billard","year":"2019","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cviu.2017.08.004","article-title":"A 3D deformable model-based framework for the retrieval of near-isometric flattenable objects using Bag-of-Visual-Words","volume":"167","author":"Rantoson","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107601","DOI":"10.1016\/j.asoc.2021.107601","article-title":"Hierarchical deep reinforcement learning to drag heavy objects by adult-sized humanoid robot","volume":"110","author":"Saeedvand","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4602052","DOI":"10.1155\/2019\/4602052","article-title":"Interactive Q-Learning Approach for Pick-and-Place Optimization of the Die Attach Process in the Semiconductor Industry","volume":"2019","author":"Ahn","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_19","first-page":"50","article-title":"Pick and Place Objects in a Cluttered Scene Using Deep Reinforcement Learning","volume":"20","author":"Mohammed","year":"2020","journal-title":"Int. J. Mech. Mechatron. Eng. IJMME"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lan, X., Qiao, Y., and Lee, B. (2021, January 4\u20136). Towards Pick and Place Multi Robot Coordination Using Multi-agent Deep Reinforcement Learning. Proceedings of the 2021 7th International Conference on Automation, Robotics and Applications (ICARA), Prague, Czech Republic.","DOI":"10.1109\/ICARA51699.2021.9376433"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"178450","DOI":"10.1109\/ACCESS.2020.3027923","article-title":"Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations","volume":"8","author":"Mohammed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nguyen, H., and La, H. (2019, January 25\u201327). Review of Deep Reinforcement Learning for Robot Manipulation. Proceedings of the 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy.","DOI":"10.1109\/IRC.2019.00120"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lobbezoo, A., Qian, Y., and Kwon, H.J. (2021). Reinforcement learning for pick and place operations in robotics: A survey. Robotics, 10.","DOI":"10.3390\/robotics10030105"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1080\/00207543.2021.1973138","article-title":"Deep reinforcement learning in production systems: A systematic literature review","volume":"60","author":"Panzer","year":"2022","journal-title":"Int. J. Prod. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cordeiro, A., Rocha, L.F., Costa, C., Costa, P., and Silva, M.F. (2022, January 29\u201330). Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey. Proceedings of the 2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Santa Maria da Feira, Portugal.","DOI":"10.1109\/ICARSC55462.2022.9784795"},{"key":"ref_26","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press. [2nd ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fran\u00e7ois-Lavet, V., Henderson, P., Islam, R., Bellemare, M.G., and Pineau, J. (2018). An Introduction to Deep Reinforcement Learning, NOW.","DOI":"10.1561\/9781680835397"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.artint.2015.04.001","article-title":"Robotic manipulation of multiple objects as a POMDP","volume":"247","author":"Pajarinen","year":"2017","journal-title":"Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abolghasemi, P., and B\u00f6l\u00f6ni, L. (August, January 31). Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197552"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zeng, A., Yu, K.-T., Song, S., Suo, D., Walker, E., Rodriguez, A., and Xiao, J. (June, January 29). Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989165"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4978","DOI":"10.1109\/LRA.2020.3004787","article-title":"Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations","volume":"5","author":"Song","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_32","first-page":"493","article-title":"Learning Pick to Place Objects using Self-supervised Learning with Minimal Training Resources","volume":"12","author":"Mohammed","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mohammed, M.Q., Kwek, L.C., Chua, S.C., and Alandoli, E.A. (2021, January 4\u20135). Color Matching Based Approach for Robotic Grasping. Proceedings of the 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen.","DOI":"10.1109\/ICOTEN52080.2021.9493540"},{"key":"ref_34","unstructured":"Florence, P.R., Manuelli, L., and Tedrake, R. (2018). Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. arXiv, 1\u201312."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Song, Y., Fei, Y., Cheng, C., Li, X., and Yu, C. (2019, January 4\u20139). UG-Net for Robotic Grasping using Only Depth Image. Proceedings of the 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), Irkutsk, Russia.","DOI":"10.1109\/RCAR47638.2019.9044116"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, X., Ye, Z., Sun, J., Fan, Y., Hu, F., Wang, C., and Lu, C. (August, January 31). Transferable Active Grasping and Real Embodied Dataset. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197185"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Corona, E., Pumarola, A., Aleny\u00e0, G., Moreno-Noguer, F., and Rogez, G. (2020, January 13\u201319). GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00508"},{"key":"ref_38","unstructured":"Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., and Vanhoucke, V. (2018, January 29\u201331). QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Proceedings of the 2nd Conference on Robot Learning, PMLR 87, Z\u00fcrich, Switzerland."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wu, B., Akinola, I., and Allen, P.K. (2019, January 3\u20138). Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968263"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wada, K., Kitagawa, S., Okada, K., and Inaba, M. (2018, January 1\u20135). Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593690"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Murali, A., Mousavian, A., Eppner, C., Paxton, C., and Fox, D. (August, January 31). 6-DOF Grasping for Target-driven Object Manipulation in Clutter. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197318"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Mousavian, A., Triebel, R., and Fox, D. (June, January 30). Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561877"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Berscheid, L., R\u00fchr, T., and Kr\u00f6ger, T. (2019, January 20\u201324). Improving Data Efficiency of Self-supervised Learning for Robotic Grasping. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793952"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Berscheid, L., Friedrich, C., and Kr\u00f6ger, T. (June, January 30). Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitives. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560901"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lou, X., Yang, Y., and Choi, C. (June, January 30). Collision-Aware Target-Driven Object Grasping in Constrained Environments. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561473"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Corsaro, M., Tellex, S., and Konidaris, G. (October, January 27). Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636876"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s10514-020-09907-y","article-title":"Generative Attention Learning: A \u201cGenerAL\u201d framework for high-performance multi-fingered grasping in clutter","volume":"44","author":"Wu","year":"2020","journal-title":"Auton. Robots"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1109\/LRA.2021.3096239","article-title":"DDGC: Generative Deep Dexterous Grasping in Clutter","volume":"6","author":"Lundell","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0278364919859066","article-title":"Learning robust, real-time, reactive robotic grasping","volume":"39","author":"Morrison","year":"2020","journal-title":"Int. J. Rob. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wada, K., Okada, K., and Inaba, M. (2019, January 20\u201324). Joint learning of instance and semantic segmentation for robotic pick-and-place with heavy occlusions in clutter. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793783"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hasegawa, S., Wada, K., Kitagawa, S., Uchimi, Y., Okada, K., and Inaba, M. (2019, January 20\u201324). GraspFusion: Realizing Complex Motion by Learning and Fusing Grasp Modalities with Instance Segmentation. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793710"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kim, T., Park, Y., Park, Y., and Suh, I.H. (2020). Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image. arXiv, 1\u20138.","DOI":"10.1109\/IROS51168.2021.9635931"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Mousavian, A., Triebel, R., and Fox, D. (2021). Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes. arXiv, 1\u20137.","DOI":"10.1109\/ICRA48506.2021.9561877"},{"key":"ref_54","first-page":"560","article-title":"What are the important technologies for bin picking? Technology analysis of robots in competitions based on a set of performance metrics","volume":"34","author":"Fujita","year":"2020","journal-title":"Adv. Robot."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mitash, C., Bekris, K.E., and Boularias, A. (2017, January 24\u201328). A self-supervised learning system for object detection using physics simulation and multi-view pose estimation. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202206"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kitagawa, S., Wada, K., Hasegawa, S., Okada, K., and Inaba, M. (2018, January 1\u20135). Multi-Stage Learning of Selective Dual-Arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593752"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shao, Q., Hu, J., Wang, W., Fang, Y., Liu, W., Qi, J., and Ma, J. (2019, January 3\u20135). Suction Grasp Region Prediction Using Self-supervised Learning for Object Picking in Dense Clutter. Proceedings of the 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR), Singapore.","DOI":"10.1109\/ICMSR.2019.8835468"},{"key":"ref_58","unstructured":"Han, M., Liu, W., Pan, Z., Xuse, T., Shao, Q., Ma, J., and Wang, W. (2019). Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning. arXiv, 1\u20136."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cao, H., Zeng, W., and Wu, I. (2022, January 23\u201327). Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811911"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Yu, K.-T., Donlon, E., Hogan, F.R., Bauza, M., Ma, D., Taylor, O., Liu, M., and Romo, E. (2018, January 21\u201325). Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461044"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/TII.2020.2969680","article-title":"An Interactive Perception Method for Warehouse Automation in Smart Cities","volume":"17","author":"Liu","year":"2021","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, Y., Guo, X., Wei, Y., Lu, K., Fang, B., Guo, D., Liu, H., and Sun, F. (2019, January 3\u20138). Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967899"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, H., Yuan, Y., Deng, Y., Guo, X., Wei, Y., Lu, K., Fang, B., Guo, D., and Sun, F. (2019). Active Affordance Exploration for Robot Grasping. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.","DOI":"10.1007\/978-3-030-27541-9_35"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Yen-Chen, L., Zeng, A., Song, S., Isola, P., and Lin, T.-Y. (August, January 31). Learning to See before Learning to Act: Visual Pre-training for Manipulation. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197331"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Welker, S., Lee, J., Rodriguez, A., and Funkhouser, T. (2018, January 1\u20135). Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593986"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ju, Z., and Yang, C. (2020, January 19\u201324). Combining Reinforcement Learning and Rule-based Method to Manipulate Objects in Clutter. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207153"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Berscheid, L., Mei\u00dfner, P., and Kr\u00f6ger, T. (2019, January 3\u20138). Robot Learning of Shifting Objects for Grasping in Cluttered Environments. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968042"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1080\/01691864.2020.1757504","article-title":"Learning efficient push and grasp policy in a totebox from simulation","volume":"34","author":"Ni","year":"2020","journal-title":"Adv. Robot."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Shang, H. (2020). Robotic pushing and grasping knowledge learning via attention deep Q-learning network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, Springer.","DOI":"10.1007\/978-3-030-55130-8_20"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Mohammed, M.Q., Kwek, L.C., Chua, S.C., Aljaloud, A.S., Al-dhaqm, A., Al-mekhlafi, Z.G., and Mohammed, B.A. (2021). Deep reinforcement learning-based robotic grasping in clutter and occlusion. Sustainability, 13.","DOI":"10.3390\/su132413686"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lu, N., Lu, T., Cai, Y., and Wang, S. (2020, January 6\u20138). Active Pushing for Better Grasping in Dense Clutter with Deep Reinforcement Learning. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.","DOI":"10.1109\/CAC51589.2020.9327270"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Goodrich, B., Kuefler, A., and Richards, W.D. (August, January 31). Depth by Poking: Learning to Estimate Depth from Self-Supervised Grasping. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196797"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/JAS.2021.1004255","article-title":"Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"8783","DOI":"10.1109\/LRA.2022.3188437","article-title":"Learning Push-Grasping in Dense Clutter","volume":"7","author":"Kiatos","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Lu, N., Cai, Y., Lu, T., Cao, X., Guo, W., and Wang, S. (2022). Picking out the Impurities: Attention-based Push-Grasping in Dense Clutter. Robotica, 1\u201316.","DOI":"10.1017\/S0263574722000297"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1038\/s41598-022-07900-2","article-title":"A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints","volume":"12","author":"Peng","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Serhan, B., Pandya, H., Kucukyilmaz, A., and Neumann, G. (2022, January 23\u201327). Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811645"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ren, D., Ren, X., Wang, X., Digumarti, S.T., and Shi, G. (2021). Fast-Learning Grasping and Pre-Grasping via Clutter Quantization and Q-map Masking. arXiv, 1\u20138.","DOI":"10.1109\/IROS51168.2021.9636165"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Gualtieri, M., ten Pas, A., and Platt, R. (2018, January 21\u201325). Pick and Place Without Geometric Object Models. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460553"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4828","DOI":"10.1109\/LRA.2020.3003865","article-title":"Self-Supervised Learning for Precise Pick-and-Place Without Object Model","volume":"5","author":"Berscheid","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Su, Y.-S., Lu, S.-H., Ser, P.-S., Hsu, W.-T., Lai, W.-C., Xie, B., Huang, H.-M., Lee, T.-Y., Chen, H.-W., and Yu, L.-F. (2019, January 3\u20138). Pose-Aware Placement of Objects with Semantic Labels-Brandname-based Affordance Prediction and Cooperative Dual-Arm Active Manipulation. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967755"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103736","DOI":"10.1016\/j.robot.2021.103736","article-title":"Hierarchical POMDP planning for object manipulation in clutter","volume":"139","author":"Zhao","year":"2021","journal-title":"Rob. Auton. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"6724","DOI":"10.1109\/LRA.2020.3015448","article-title":"\u201cGood Robot!\u201d: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer","volume":"5","author":"Hundt","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Li, R., Jabri, A., Darrell, T., and Agrawal, P. (August, January 31). Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197468"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Huang, E., Jia, Z., and Mason, M.T. (2019, January 20\u201324). Large-scale multi-object rearrangement. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793946"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.robot.2019.06.007","article-title":"End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer","volume":"119","author":"Yuan","year":"2019","journal-title":"Rob. Auton. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Song, H., Haustein, J.A., Yuan, W., Hang, K., Wang, M.Y., Kragic, D., and Stork, J.A. (2020, January 25\u201329). Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341532"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Rouillard, T., Howard, I., and Cui, L. (2019, January 4\u20137). Autonomous Two-Stage Object Retrieval Using Supervised and Reinforcement Learning. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.","DOI":"10.1109\/ICMA.2019.8816290"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Chen, C., Li, H.-Y., Zhang, X., Liu, X., and Tan, U.-X. (2019, January 21\u201322). Towards Robotic Picking of Targets with Background Distractors using Deep Reinforcement Learning. Proceedings of the 2019 WRC Symposium on Advanced Robotics and Automation (WRC SARA), Beijing, China.","DOI":"10.1109\/WRC-SARA.2019.8931932"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Novkovic, T., Pautrat, R., Furrer, F., Breyer, M., Siegwart, R., and Nieto, J. (August, January 31). Object Finding in Cluttered Scenes Using Interactive Perception. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197101"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/LRA.2020.2970622","article-title":"A Deep Learning Approach to Grasping the Invisible","volume":"5","author":"Yang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Zuo, G., Tong, J., Wang, Z., and Gong, D. (2022). A Graph-Based Deep Reinforcement Learning Approach to Grasping Fully Occluded Objects. Cognit. Comput.","DOI":"10.1007\/s12559-022-10047-x"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Fujita, Y., Uenishi, K., Ummadisingu, A., Nagarajan, P., Masuda, S., and Castro, M.Y. (2020, January 25\u201329). Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341605"},{"key":"ref_94","unstructured":"Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., and Zaremba, W. (2017, January 4\u20139). Hindsight experience replay. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA. Available online: https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-85047009130&partnerID=40&md5=ca73138ba801e435530b77496eeafe86."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kurenkov, A., Taglic, J., Kulkarni, R., Dominguez-Kuhne, M., Garg, A., Mart\u00edn-Mart\u00edn, R., and Savarese, S. (2021, January 25\u201329). Visuomotor mechanical search: Learning to retrieve target objects in clutter. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341545"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Huang, B., Guo, T., Boularias, A., and Yu, J. (2022, January 23\u201327). Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812132"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Kumar, K.N., Essa, I., and Ha, S. (2022, January 23\u201327). Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811874"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Danielczuk, M., Angelova, A., Vanhoucke, V., and Goldberg, K. (2021, January 25\u201329). X-Ray: Mechanical search for an occluded object by minimizing support of learned occupancy distributions. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340984"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Deng, Y., Guo, D., Guo, X., Zhang, N., Liu, H., and Sun, F. (July, January 27). MQA: Answering the Question via Robotic Manipulation. Proceedings of the Robotics: Science and Systems (RSS 2021), New York, NY, USA.","DOI":"10.15607\/RSS.2021.XVII.044"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"6337","DOI":"10.1109\/LRA.2021.3092640","article-title":"Efficient learning of goal-oriented push-grasping synergy in clutter","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/LRA.2021.3123373","article-title":"Visual Foresight Trees for Object Retrieval From Clutter With Nonprehensile Rearrangement","volume":"7","author":"Huang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Bejjani, W., Agboh, W.C., Dogar, M.R., and Leonetti, M. (October, January 27). Occlusion-Aware Search for Object Retrieval in Clutter. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636230"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s11370-021-00377-4","article-title":"Obstacle rearrangement for robotic manipulation in clutter using a deep Q-network","volume":"14","author":"Cheong","year":"2021","journal-title":"Intell. Serv. Robot."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Bejjani, W., Papallas, R., Leonetti, M., and Dogar, M.R. (2018, January 6\u20139). Planning with a Receding Horizon for Manipulation in Clutter Using a Learned Value Function. Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), Beijing, China.","DOI":"10.1109\/HUMANOIDS.2018.8624977"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Bejjani, W., Dogar, M.R., and Leonetti, M. (2019, January 3\u20138). Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967717"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"103730","DOI":"10.1016\/j.robot.2021.103730","article-title":"Learning image-based Receding Horizon Planning for manipulation in clutter","volume":"138","author":"Bejjani","year":"2021","journal-title":"Rob. Auton. Syst."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Wu, P., Chen, W., Liu, H., Duan, Y., Lin, N., and Chen, X. (2019, January 21\u201322). Predicting Grasping Order in Clutter Environment by Using Both Color Image and Points Cloud. Proceedings of the 2019 WRC Symposium on Advanced Robotics and Automation (WRC SARA), Beijing, China.","DOI":"10.1109\/WRC-SARA.2019.8931929"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Papallas, R., and Dogar, M.R. (August, January 31). Non-Prehensile Manipulation in Clutter with Human-In-The-Loop. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196689"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"5377","DOI":"10.1109\/LRA.2020.3006826","article-title":"Online replanning with human-in-The-loop for non-prehensile manipulation in clutter-A trajectory optimization based approach","volume":"5","author":"Papallas","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Kiatos, M., and Malassiotis, S. (2019, January 20\u201324). Robust object grasping in clutter via singulation. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793972"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Sarantopoulos, I., Kiatos, M., Doulgeri, Z., and Malassiotis, S. (August, January 31). Split Deep Q-Learning for Robust Object Singulation*. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196647"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"4117","DOI":"10.1109\/LRA.2021.3062295","article-title":"Total Singulation With Modular Reinforcement Learning","volume":"6","author":"Sarantopoulos","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_113","unstructured":"Tekden, A.E., Erdem, A., Erdem, E., Asfour, T., and Ugur, E. (2021). Object and Relation Centric Representations for Push Effect Prediction. arXiv, 1\u201312."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Won, J., Park, Y., Yi, B.-J., and Suh, I.H. (2019, January 3\u20138). Object Singulation by Nonlinear Pushing for Robotic Grasping. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968077"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TRO.2020.3033696","article-title":"A Geometric Approach for Grasping Unknown Objects With Multifingered Hands","volume":"37","author":"Kiatos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Mahler, J., Liang, J., Niyaz, S., Aubry, M., Laskey, M., Doan, R., Liu, X., Ojea, J.A., and Goldberg, K. (2017, January 12\u201316). Dex-Net 2.0: Deep learning to plan Robust grasps with synthetic point clouds and analytic grasp metrics. Proceedings of the 2017 Robotics: Science and Systems (RSS), Cambridge, MA, USA.","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"ref_117","unstructured":"Mousavian, A., Eppner, C., and Fox, D. (November, January 27). 6-DOF GraspNet: Variational grasp generation for object manipulation. Proceedings of the the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Iriondo, A., Lazkano, E., and Ansuategi, A. (2021). Affordance-based grasping point detection using graph convolutional networks for industrial bin-picking applications. Sensors, 21.","DOI":"10.3390\/s21030816"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"6795","DOI":"10.1109\/TIM.2020.2976420","article-title":"Random Cropping Ensemble Neural Network for Image Classification in a Robotic Arm Grasping System","volume":"69","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"101888","DOI":"10.1016\/j.rcim.2019.101888","article-title":"A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper","volume":"63","author":"Tripicchio","year":"2020","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"68277","DOI":"10.1109\/ACCESS.2021.3077117","article-title":"Dynamics Learning With Object-Centric Interaction Networks for Robot Manipulation","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_122","unstructured":"Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., and Wermter, S. (2021). Survey on reinforcement learning for language processing. arXiv, 1\u201333."},{"key":"ref_123","unstructured":"Sajjan, S., Moore, M., Pan, M., Nagaraja, G., Lee, J., Zeng, A., and Song, S. (August, January 31). Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation. Proceedings of the IEEE International Conference on Robotics and Automation, Paris, France."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.1109\/LRA.2019.2930476","article-title":"3-D Deformable Object Manipulation Using Deep Neural Networks","volume":"4","author":"Hu","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"129338","DOI":"10.1109\/ACCESS.2020.3008763","article-title":"Grasping Objects Mixed with Towels","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"11748","DOI":"10.1109\/JSEN.2020.3035632","article-title":"BiLuNetICP: A Deep Neural Network for Object Semantic Segmentation and 6D Pose Recognition","volume":"21","author":"Tran","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wu, J., Zeng, A., Tenenbaum, J., and Song, S. (2019). DensePhysNet: Learning Dense Physical Object Representations Via Multi-Step Dynamic Interactions. arXiv, 1\u201310.","DOI":"10.15607\/RSS.2019.XV.046"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Zakka, K., Zeng, A., Lee, J., and Song, S. (August, January 31). Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly. Proceedings of the Proceeding of 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196733"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Wang, C., and Lin, P. (2020, January 6\u20139). Q-PointNet: Intelligent Stacked-Objects Grasping Using a RGBD Sensor and a Dexterous Hand. Proceedings of the 2020 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA.","DOI":"10.1109\/AIM43001.2020.9158850"},{"key":"ref_130","unstructured":"Ni, P., Zhang, W., Zhu, X., and Cao, Q. (August, January 31). PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds. Proceedings of the IEEE International Conference on Robotics and Automation, Paris, France."},{"key":"ref_131","unstructured":"Wu, B., Akinola, I., Varley, J., and Allen, P. (2019). MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning. arXiv, 1\u201320."},{"key":"ref_132","unstructured":"Schnieders, B., Palmer, G., Luo, S., and Tuyls, K. (2019, January 13\u201317). Fully convolutional one-shot object segmentation for industrial robotics. Proceedings of the the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, Montreal, QC, Canada."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Morrison, D., Leitner, J., and Corke, P. (2018, January 26\u201330). Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach. Proceedings of the Robotics: Science and Systems XIV (RSS 2018), Pittsburgh, PA, USA.","DOI":"10.15607\/RSS.2018.XIV.021"},{"key":"ref_134","unstructured":"Calandra, R., Owens, A., Upadhyaya, M., Yuan, W., Lin, J., Adelson, E.H., and Levine, S. (2017, January 13\u201315). The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?. Proceedings of the the Conference on Robot Learning (CoRL), Mountain View, CA, USA."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Eitel, A., Hauff, N., and Burgard, W. (2019, January 3\u20138). Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Macau, China.","DOI":"10.1109\/IROS40897.2019.8967915"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Li, A., Danielczuk, M., and Goldberg, K. (2020, January 20\u201321). One-Shot Shape-Based Amodal-to-Modal Instance Segmentation. Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Hong Kong, China.","DOI":"10.1109\/CASE48305.2020.9216733"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Nematollahi, I., Mees, O., Hermann, L., and Burgard, W. (2020, January 25\u201329). Hindsight for foresight: Unsupervised structured dynamics models from physical interaction. Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341491"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T21:32:26Z","timestamp":1723066346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/7938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"references-count":137,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22207938"],"URL":"https:\/\/doi.org\/10.3390\/s22207938","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,18]]}}}