A Survey on Learning-Based Robotic Grasping

Current Robotics Reports - Tập 1 - Trang 239-249 - 2020
Kilian Kleeberger1, Richard Bormann1, Werner Kraus1, Marco F. Huber1,2
1Fraunhofer IPA, Stuttgart, Germany
2IFF, University of Stuttgart, Stuttgart, Germany

Tóm tắt

This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.

Tài liệu tham khảo

Hodson R. A gripping problem: designing machines that can grasp and manipulate objects with anything approaching human levels of dexterity is first on the to-do list for robotics. In: Nature; 2018. Zeng A, Song S, Yu K-T, Donlon E, Hogan FR, Bauza M, et al. Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. PiscatawayJ: IEEE; 2018. Kumra S, Kanan C. Robotic grasp detection using deep convolutional neural networks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); September 24–28, 2017; Vancouver: IEEE; 2017. Reinhart G, Hüttner S, Krug S. Automatic configuration of robot systems – upward and downward integration. In: Jeschke S, Liu H, Schilberg D, editors. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. El-Shamouty M, Kleeberger K, Lämmle A, Huber M. Simulation-driven machine learning for robotics and automation. tm - Technisches Messen. 2019;86:673–84. Bohg J, Morales A, Asfour T, Kragic D. Data-driven grasp synthesis—a survey. In: IEEE Transactions on Robotics (T-RO); 2014. Sahbani A, El-Khoury S, Bidaud P. An overview of 3D object grasp synthesis algorithms. In: Robotics and Autonomous Systems; 2012. Bicchi A, Kumar V. Robotic grasping and contact: a review. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); April 24–28, 2000; San Francisco, CA, USA; 2000. Shimoga KB. Robot grasp synthesis algorithms: a survey. In: The International Journal of Robotics Research (IJRR); 1996. Bormann R, Brito BF de, Lindermayr J, Omainska M, Patel M. Towards automated order picking robots for warehouses and retail. In: Tzovaras, Dimitrios and Giakoumis, Dimitrios and Vincze, Markus and Argyros, Antonis, editor. Computer Vision Systems; September 23–25, 2019; Thessaloniki, Greece. Cham: Springer International Publishing; 2019. Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge Massachusetts: The MIT Press; 2018. Mahler J, Liang J, Niyaz S, Laskey M, Doan R, Liu X, et al. Dex-Net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp Metrics. In: Amato N, Srinivasa S, Ayanian N, Kuindersma S, editors. Robotics: Science and Systems (RSS); July 12–16, 2017; Cambridge, Massachusetts, USA: Robotics Science and Systems Foundation; 2017. Mahler J, Matl M, Liu X, Li A, Gealy D, Goldberg K. Dex-Net 3.0: computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Redmon J, Angelova A. Real-time grasp detection using convolutional neural networks. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 26–30, 2015; Seattle, WA, USA; 2015. Morrison D, Leitner J, Corke P. Closing the loop for robotic grasping: a real-time, generative grasp synthesis approach. In: Kress-Gazit H, Srinivasa S, Atanasov N, editors. Robotics: Science and Systems (RSS); June 26–30, 2018. Pittsburgh: Robotics Science and Systems Foundation; 2018. James S, Wohlhart P, Kalakrishnan M, Kalashnikov D, Irpan A, Ibarz J, et al. Sim-to-real via sim-to-sim: data-efficient robotic grasping via randomized-to-canonical adaptation networks. In: IEEE, editor. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 16–20, 2019; Long Beach, CA; 2019. Siciliano B, Khatib O, editors. Springer Handbook of Robotics. Berlin: Springer Science+Business Media; 2008. Tremblay J, To T, Sundaralingam B, Xiang Y, Fox D, Birchfield S. Deep object pose estimation for semantic robotic grasping of household objects. In: Conference on Robot Learning (CoRL); October 29–31, 2018. Zürich: PMLR; 2018. Dong Z, Liu S, Zhou T, Cheng H, Zeng L, Yu X, Liu H. PPR-Net: point-wise pose regression network for instance segmentation and 6D pose estimation in bin-picking scenarios. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); November 4–8, 2019; The Venetian Macao, Macau, China: IEEE; 2019. • Kleeberger K, Huber MF. Single shot 6D object pose estimation. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 31 – June 4, 2020; Palais des Congrès de Paris, France; 2020. Provides state-of-the-art results for 6D object pose estimation in highly cluttered scenes. Lenz I, Lee H, Saxena A. Deep learning for detecting robotic grasps. In: The International Journal of Robotics Research (IJRR); 2015. Morrison D, Corke P, Leitner J. Learning robust, real-time, reactive robotic grasping. In: The International Journal of Robotics Research (IJRR); 2019. Pinto L, Gupta A. Supersizing self-supervision: learning to grasp from 50K tries and 700 robot hours. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 16–21, 2016; Stockholm, Sweden; 2016. Levine S, Pastor P, Krizhevsky A, Quillen D. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. In: International Symposium on Experimental Robotics (ISER); 2016. • Levine S, Pastor P, Krizhevsky A, Ibarz J, Quillen D. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. In: The International Journal of Robotics Research (IJRR); 2018. Highly influential work demonstrating the potential of deep learning for robotic grasping. Mahler J, Pokorny FT, Hou B, Roderick M, Laskey M, Aubry M, et al. Dex-Net 1.0: a cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 16–21, 2016; Stockholm, Sweden; 2016. Mahler J, Matl M, Satish V, Danielczuk M, DeRose B, McKinley S, Goldberg K. Learning ambidextrous robot grasping policies. SCIENCE ROBOTICS. 2019. Satish V, Mahler J, Goldberg K. On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks. In: IEEE Robotics and Automation Letters; 2019. •• Kalashnikov D, Irpan A, Pastor P, Ibarz J, Herzog A, Jang E, et al. QT-Opt: scalable deep reinforcement learning for vision-based robotic manipulation. In: Conference on Robot Learning (CoRL); October 29–31, 2018; Zürich, Switzerland: PMLR; 2018. Setting a milestone in robotic grasping and manipulation. Zeng A, Song S, Lee J, Rodriguez A, Funkhouser TA. TossingBot: learning to throw arbitrary objects with residual physics. In: Bicchi A, Kress-Gazit H, Hutchinson S, editors. Robotics: Science and Systems (RSS); June 22–26, 2019; Messe Freiburg, Germany; 2019. Qin Y, Chen R, Zhu H, Song M, Xu J. S4G: amodal single-view single-shot SE(3) grasp detection in cluttered scenes. In: Conference on Robot Learning (CoRL); October 30 – November 1, 2019; Osaka, Japan; 2019. • Mousavian A, Eppner C, Fox D. 6-DOF GraspNet: variational grasp generation for object manipulation. In: IEEE, editor. IEEE International Conference on Computer Vision (ICCV); October 27 – November 2, 2019; Seoul, Korea; 2019. Addresses model-free grasping in 6D. •• Song S, Zeng A, Lee J, Funkhouser T. Grasping in the Wild:learning 6DoF closed-loop grasping from low-cost demonstrations. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 31 – June 4, 2020; Palais des Congrès de Paris, France; 2020. Addresses closed-loop model-free grasping in 6D and in cluttered scenes. ten Pas A, Gualtieri M, Saenko K, Platt R. Grasp pose detection in point clouds. In: The International Journal of Robotics Research (IJRR); 2017. Spenrath F, Pott A. Gripping point determination for bin picking using heuristic search. In: CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME); July 20–22, 2016; Ischia, Italy; 2016. Brégier R, Devernay F, Leyrit L, Crowley JL. Symmetry aware evaluation of 3D object detection and pose estimation in scenes of many parts in bulk. In: IEEE, editor. IEEE International Conference on Computer Vision (ICCV); October 22–29, 2017; Venice, Italy; 2017. Brégier R, Devernay F, Leyrit L, Crowley JL. Defining the pose of any 3D rigid object and an associated distance. In: International Journal of Computer Vision (IJCV); 2018. Hodaň T, Matas J, Obdržálek Š. On evaluation of 6D object pose estimation. In: European Conference on Computer Vision (ECCV); 2016. Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N. Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Asian Conference on Computer Vision (ACCV); 2012. Spenrath F, Pott A. Using neural networks for heuristic grasp planning in random bin picking. In: IEEE International Conference on Automation Science and Engineering (CASE); August 20–24, 2018; Munich, Germany; 2018. Ledermann T. Partikel-Schwarm-Optimierung zur Objektlageerkennung in Tiefendaten [Dissertation]. Stuttgart: University of Stuttgart; 2012. Palzkill M. Heuristisches Suchverfahren zur Objektlageerkennung aus Punktewolken für industrielle Zuführsysteme [Dissertation]. Stuttgart: University of Stuttgart; 2014. Kleeberger K, Landgraf C, Huber MF. Large-scale 6D object pose estimation dataset for industrial bin-picking. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); November 4–8, 2019. IEEE: The Venetian Macao, Macau, China; 2019. Kehl W, Manhardt F, Tombari F, Ilic S, Navab N. SSD-6D: making RGB-based 3D detection and 6D pose estimation great again. In: IEEE, editor. IEEE International Conference on Computer Vision (ICCV); October 22–29, 2017; Venice, Italy; 2017. Sundermeyer M, Marton Z, Durner M, Triebel R. Implicit 3D orientation learning for 6D object detection from RGB images. In: European Conference on Computer Vision (ECCV); 2018. Tekin B, Sinha SN, Fua P. Real-time seamless single shot 6D object pose prediction. In: IEEE, editor. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 18–22, 2018; Salt Lake City, Utah; 2018. Lepetit V, Moreno-Noguer F, Fua P. EPnP: an accurate O(n) solution to the PnP problem. In: International Journal of Computer Vision (IJCV); 2009. •• Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P. Domain randomization for transferring deep neural networks from simulation to the real world. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); September 24–28, 2017; Vancouver, BC, Canada: IEEE; 2017. Highly influential work regarding sim-to-real transfer. Kleeberger K, Huber MF. Object pose estimation challenge for bin-picking. 2019. https://www.bin-picking.ai/en/competition.html. Accessed 1 June 2020. Hodaň T, Michel F, Sahin C, Kim T-K, Matas J, Rother C. SIXD Challenge 2017. 2017. http://cmp.felk.cvut.cz/sixd/challenge2017/. Accessed 1 June 2020. Hodaň T, Michel F, Brachmann E, Kehl W, Glent Buch A, Kraft D, et al. BOP: benchmark for 6D object pose estimation. In: European Conference on Computer Vision (ECCV); 2018. Accessed 1 June 2020. Qi CR, Yi L, Su H, Guibas LJ. PointNet++: deep hierarchical feature learning on point sets in a metric space. In: I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett, editors. Advances in Neural Information Processing Systems 30 (NIPS 2017); December 04–09, 2017. Long Beach, California; 2017. Saxena A, Driemeyer J, Ng AY. Robotic grasping of novel objects using vision. In: The International Journal of Robotics Research (IJRR); 2008. Rubinstein RY, Kroese DP. The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo Simulation and Machine Learning. Berlin: Springer-Verlag; 2004. Jiang Y, Moseson S, Saxena A. Efficient grasping from RGBD images: learning using a new rectangle representation. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 9–13, 2011; Shanghai, China. Piscataway, NJ: IEEE; 2011. Redmon J, Divvala S, Girshick R, Farhadi A. You Only Look Once: unified, real-time object detection. In: IEEE, editor. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 26 – July 1, 2016; Las Vegas, Nevada; 2016. Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: IEEE, editor. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); July 21–26, 2017; Honolulu, Hawaii; 2017. 7263–7271. Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection. In: C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger, editors. Advances in Neural Information Processing Systems 26 (NIPS 2013): Curran Associates, Inc; 2013. Cornell University. Cornell Grasping Dataset. http://pr.cs.cornell.edu/grasping/rectdata/data.php. Accessed 1 June 2020. Depierre A, Dellandréa E, Chen L. Jacquard: a large scale dataset for robotic grasp detection. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 1–5, 2018; Madrid, Spain: IEEE; 2018. Zeng A, Song S, Welker S, Lee J, Rodriguez A, Funkhouser TA. Learning synergies between pushing and grasping with self-supervised deep reinforcement learning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 1–5, 2018; Madrid, Spain: IEEE; 2018. Berscheid L, Meißner P, Kroeger T. Robot learning of shifting objects for grasping in cluttered environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); November 4–8, 2019; The Venetian Macao, Macau, China: IEEE; 2019. Quillen D, Jang E, Nachum O, Finn C, Ibarz J, Levine S. Deep reinforcement learning for vision-based robotic grasping: a simulated comparative evaluation of off-policy methods. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. • Bousmalis K, Irpan A, Wohlhart P, Bai Y, Kelcey M, Kalakrishnan M, et al. Using simulation and domain adaptation to improve efficiency of deep robotic grasping. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Highly influential work regarding sim-to-real transfer for robotic grasping. Rohmer E, Singh SPN, Freese M. V-REP: a versatile and scalable robot simulation framework. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); November 3–7, 2013; Tokyo, Japan: IEEE; 2013. James S, Freese M, Davison AJ. PyRep: bringing V-REP to deep robot learning; 26.06.2019. Todorov E, Erez T, Tassa Y. MuJoCo: a physics engine for model-based control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 7–12, 2012; Vilamoura, Algarve, Portugal: IEEE; 2012. Blender. https://www.blender.org/. Accessed 1 June 2020. Koenig N, Howard A. Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 28 September – 2 October, 2004; Sendai, Japan: IEEE; 2004. p. 2149–2154. https://doi.org/10.1109/IROS.2004.1389727. Accessed 1 June 2020. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D. Unsupervised pixel-level domain adaptation with generative adversarial networks. In: IEEE, editor. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); July 21–26, 2017; Honolulu, Hawaii; 2017. Peng XB, Andrychowicz M, Zaremba W, Abbeel P. Sim-to-real transfer of robotic control with dynamics randomization. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R. Learning from simulated and unsupervised images through adversarial training. In: IEEE, editor. IEEE International Conference on Computer Vision (ICCV); October 22–29, 2017; Venice, Italy; 2017. Fang K, Bai Y, Hinterstoisser S, Savarese S, Kalakrishnan M. Multi-task domain adaptation for deep learning of instance grasping from simulation. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Danielczuk M, Matl M, Gupta S, Li A, Lee A, Mahler J, Goldberg K. Segmenting unknown 3D objects from real depth images using Mask R-CNN trained on synthetic data. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 20–24, 2019; Montreal, Canada; 2019. James S, Davison AJ, Johns E. Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task. In: Conference on Robot Learning (CoRL); November 13–15, 2017; Mountain View, California: PMLR; 2017. Chebotar Y, Handa A, Makoviychuk V, Macklin M, Issac J, Ratliff N, Fox D. Closing the sim-to-real loop: adapting simulation randomization with real world experience. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 20–24, 2019; Montreal, Canada; 2019. OpenAI, Andrychowicz M, Baker B, Chociej M, Jozefowicz R, Mc Grew B, et al. Learning dexterous in-hand manipulation; 01.08.2018. OpenAI, Akkaya I, Andrychowicz M, Chociej M, Litwin M, McGrew B, et al. Solving Rubik’s Cube with a robot hand; 16.10.2019. Tobin J, Biewald L, Duan R, Andrychowicz M, Handa A, Kumar V, et al. Domain randomization and generative models for robotic grasping. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 1–5, 2018; Madrid, Spain: IEEE; 2018. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, et al. Domain-adversarial training of neural networks. In: Journal of Machine Learning Research 17; 2016. Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D. Domain separation networks. In: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett, editors. Advances in Neural Information Processing Systems 29 (NIPS 2016): Curran Associates, Inc; 2016. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger, editors. Advances in Neural Information Processing Systems 27 (NIPS 2014); December 08–13, 2014. Palais des Congrès de Montréal, Montréal Canada: Curran Associates, Inc; 2014. Visual Learning Lab Heidelberg. Occluded Object Challenge. 2015. https://hci.iwr.uni-heidelberg.de/vislearn/iccv2015-occlusion-challenge/. Accessed 1 June 2020. Sun Y, Falco J, editors. Robotic grasping and manipulation: first robotic grasping and manipulation challenge, RGMC 2016, Held in Conjunction with IROS 2016, Daejeon, South Korea, October 10–12, 2016, Revised Papers. Cham: Springer; 2018. Sun Y, Calli B, Falco J, Leitner J, Roa M, Xiong R, Yokokohji Y. Robotic grasping and manipulation competition. 2019. https://rpal.cse.usf.edu/competitioniros2019/. Accessed 1 June 2020. Eppner C, Höfer S, Jonschkowski R, Martín-Martín R, Sieverling A, Wall V, Brock O. Lessons from the Amazon Picking Challenge: four aspects of building robotic systems. In: Hsu D, Amato N, Berman S, Jacobs S, editors. Robotics: Science and Systems (RSS); June 18–22, 2016; Ann Arbor, Michigan, USA; 2016. Zeng A, Yu K-T, Song S, Suo D, Walker E, JR., Rodriguez A, Xiao J. Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 29 – June 3, 2017; Singapore, Singapore: IEEE; 2017. Morrison D, Tow AW, McTaggart M, Smith R, Kelly-Boxall N, Wade-McCue S, et al. Cartman: the low-cost cartesian manipulator that won the Amazon Robotics Challenge. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Hernandez C, Bharatheesha M, Ko W, Gaiser H, Tan J, van Deurzen K, et al. Team Delft’s robot winner of the Amazon Picking Challenge 2016; 18.10.2016. Jonschkowski R, Eppner C, Hofer S, Martin-Martin R, Brock O. Probabilistic multi-class segmentation for the Amazon Picking Challenge. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); October 9–14, 2016; Daejeon, South Korea: IEEE; 2016. Correll N, Bekris KE, Berenson D, Brock O, Causo A, Hauser K, et al. Analysis and observations from the first Amazon Picking Challenge. In: IEEE Transactions on Automation Science and Engineering. p. 172–188. Leitner J, Tow AW, Dean JE, Suenderhauf N, Durham JW, Cooper M, et al. The ACRV Picking Benchmark (APB): a robotic shelf picking benchmark to foster reproducible research. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 29 – June 3, 2017; Singapore, Singapore: IEEE; 2017. Ulbrich S, Kappler D, Asfour T, Vahrenkamp N, Bierbaum A, Przybylski M, Dillmann R. The OpenGRASP benchmarking suite: an environment for the comparative analysis of grasping and dexterous manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); September 25–30, 2011; San Francisco, CA, USA: IEEE; 2011. Yan X, Hsu J, Khansari M, Bai Y, Pathak A, Gupta A, et al. Learning 6-DOF grasping interaction via deep geometry-aware 3D representations. In: IEEE, editor. IEEE International Conference on Robotics and Automation (ICRA); May 21–25, 2018; Brisbane, QLD, Australia. Piscataway, NJ: IEEE; 2018. Zhou Y, Hauser K. 6DOF grasp planning by optimizing a deep learning scoring function. In: Amato N, Srinivasa S, Ayanian N, Kuindersma S, editors. Robotics: Science and Systems (RSS); July 12–16, 2017. Cambridge: Robotics Science and Systems Foundation; 2017. Riedlinger MA, Völk M, Kleeberger K, Khalid MU, Bormann R. Model-free grasp learning framework based on physical simulation. In: International Symposium on Robotics (ISR). Munich, Germany; 2020. Gualtieri M, Platt R. Learning 6-DoF grasping and pick-place using attention focus. In: Conference on Robot Learning (CoRL); October 29–31, 2018; Zürich, Switzerland: PMLR; 2018. Jang E, Vijayanarasimhan S, Pastor P, Ibarz J, Levine S. End-to-end learning of semantic grasping. In: Conference on Robot Learning (CoRL); November 13–15, 2017; Mountain View, California: PMLR; 2017. Matsumura R, Domae Y, Wan W, Harada K. Learning based robotic bin-picking for potentially tangled objects. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); November 4–8, 2019; The Venetian Macao, Macau, China: IEEE; 2019. Moosmann M, Spenrath F, Kleeberger K, Khalid MU, Mönnig M, Rosport J, Bormann R. Increasing the robustness of random bin picking by avoiding grasps of entangled workpieces. In: CIRP Conference on Manufacturing Systems (CIRP CMS); July 1–3, 2020; Chicago, IL, US; 2020.