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We approach the problem of high-DOF reaching-and-grasping via learning joint planning of grasp and motion with deep reinforcement learning.
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Leveraging big data for grasp planning. In 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 4304–4311
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Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods. In 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 6284–6291
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Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands
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Vision-based grasp learning of an anthropomorphic hand-arm system in a synergy-based control framework
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Learning Deep Visuomotor Policies for Dexterous Hand Manipulation. In International Conference on Robotics and Automation (ICRA)
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High precision grasp pose detection in dense clutter. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 598–605
Marcus Gualtieri, Andreas Ten Pas, Kate Saenko, and Robert Platt. 2016 · 2016
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Yale-CMU-Berkeley dataset for robotic manipulation research
Berk Calli, Arjun Singh, James Bruce, Aaron Walsman, Kurt Konolige, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar. 2017 · 2017
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A Framework for Optimal Grasp Contact Planning
Kaiyu Hang, Johannes A. Stork, Nancy S. Pollard, and Danica Kragic. 2017 · 2017
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Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics. In Proceedings of Robotics: Science and Systems
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio, and Ken Goldberg. 2017 · 2017
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Understanding and exploiting object interaction landscapes
Sören Pirk, Vojtech Krs, Kaimo Hu, Suren Deepak Rajasekaran, Hao Kang, Yusuke Yoshiyasu, Bedrich Benes, and Leonidas J Guibas. 2017 · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition . 652–660
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017 · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine. 2017 · 2017
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Divye Jain, Andrew Li, Shivam Singhal, Aravind Rajeswaran, Vikash Kumar, and Emanuel Todorov. 2019 · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks. In Proc. IEEE Conf. on Computer Vision & Pattern Recognition . 12627–12637
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis. 2019 · 2019
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Grasping Unknown Objects by Exploiting Complementarity with Robot Hand Geometry. In International Conference on Computer Vision Systems . Springer, 88–97
Marios Kiatos and Sotiris Malassiotis. 2019 · 2019
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Generating Grasp Poses for A High-DOF Gripper Using Neural Networks. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems . 1518–1525
Min Liu, Zherong Pan, Kai Xu, Kanishka Ganguly, and Dinesh Manocha. 2019 · 2019
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Multifunctional principal component analysis for human-like grasping
Marco Monforte, Fanny Ficuciello, and Bruno Siciliano. 2019 · 2019
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Neural state machine for character-scene interactions
Sebastian Starke, He Zhang, Taku Komura, and Jun Saito. 2019 · 2019
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Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
Mark Van der Merwe, Qingkai Lu, Balakumar Sundaralingam, Martin Matak, and Tucker Hermans. 2019 · 2019
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Manipulation trajectory optimization with online grasp synthesis and selection
Lirui Wang, Yu Xiang, and Dieter Fox. 2019 · 2019
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
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ContactPose: A dataset of grasps with object contact and hand pose. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16 . Springer, 361–378
Samarth Brahmbhatt, Chengcheng Tang, Christopher D Twigg, Charles C Kemp, and James Hays. 2020 · 2020
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Grasping Field: Learning Implicit Representations for Human Grasps
Korrawe Karunratanakul, Jinlong Yang, Yan Zhang, Michael Black, Krikamol Muandet, and Siyu Tang. 2020 · 2020
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A survey on learning-based robotic grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus, and Marco F Huber. 2020 · 2020
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Deep Differentiable Grasp Planner for High-DOF Grippers. In Proceedings of the Robotics: Science and Systems 2020
Min Liu, Zherong Pan, Kai Xu, Kanishka Ganguly, and Dinesh Manocha. 2020b · 2020
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New Formulation of Mixed-Integer Conic Programming for Globally Optimal Grasp Planning
Min Liu, Zherong Pan, Kai Xu, and Dinesh Manocha. 2020a · 2020
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Multifingered Grasp Planning via Inference in Deep Neural Networks: Outperforming Sampling by Learning Differentiable Models
Qingkai Lu, Mark Van der Merwe, Balakumar Sundaralingam, and Tucker Hermans. 2020b · 2020
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Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations
Shuran Song, Andy Zeng, Johnny Lee, and Thomas Funkhouser. 2020 · 2020
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Dexterous robotic grasping with object-centric visual affordances. In 2021 IEEE international conference on robotics and automation (ICRA) . IEEE
Priyanka Mandikal and Kristen Grauman. 2021 · 2021
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Adagrasp: Learning an adaptive gripper-aware grasping policy. In 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 4620–4626
Zhenjia Xu, Beichun Qi, Shubham Agrawal, and Shuran Song. 2021 · 2021
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Planning of Power Grasps Using Infinite Program Under Complementary Constraints
Zherong Pan, Duo Zhang, Changhe Tu, and Xifeng Gao. 2022 · 2022
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Efficient multi-view object recognition and full pose estimation. In 2010 IEEE International Conference on Robotics and Automation . IEEE, 2050–2055
Alvaro Collet and Siddhartha S Srinivasa. 2010 · 2055
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