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Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or more general tool-use.
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep residual learning for image recognition, 2016
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Volumetric hierarchical approximate convex decomposition
Khaled Mamou, E Lengyel, and AK Peters · 2016
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Rigid body dynamic simulation with line and surface contact
Jiayin Xie and Nilanjan Chakraborty · 2016
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
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A mathematical introduction to robotic manipulation
Richard M Murray, Zexiang Li, and S Shankar Sastry · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Grasp pose detection in point clouds
Andreas ten Pas, Marcus Gualtieri, Kate Saenko, and Robert Platt · 2017
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A tutorial on Newton methods for constrained trajectory optimization and relations to SLAM, Gaussian Process smoothing, optimal control, and probabilistic inference
Marc Toussaint · 2017
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Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
Peter Florence, Lucas Manuelli, and Russ Tedrake · 2018
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Differentiable physics and stable modes for tool-use and manipulation planning
Marc Toussaint, Kelsey Allen, Kevin A Smith, and Joshua B Tenenbaum · 2018
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Self-supervised correspondence in visuomotor policy learning
Peter Florence, Lucas Manuelli, and Russ Tedrake · 2019
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kPAM: Keypoint affordances for category-level robotic manipulation
Lucas Manuelli, Wei Gao, Peter Florence, and Russ Tedrake · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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6-dof GraspNet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
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DeepSDF: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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PIFu: Pixel-aligned implicit function for high-resolution clothed human digitization
Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhoefer, and Gordon Wetzstein · 2019
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DISN: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
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SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2020
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Volumetric grasping network: Real-time 6 dof grasp detection in clutter
Michel Breyer, Jen Jen Chung, Lionel Ott, Siegwart Roland, and Nieto Juan · 2020
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Category level object pose estimation via neural analysis-by-synthesis
Pybullet, a python module for physics simulation for games, robotics and machine learning, 2016–2021
Erwin Coumans and Yunfei Bai · 2021
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Learning geometric reasoning and control for long-horizon tasks from visual input
Danny Driess*, Jung-Su Ha*, Russ Tedrake, and Marc Toussaint · 2021
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ACRONYM: A large-scale grasp dataset based on simulation
Clemens Eppner, Arsalan Mousavian, and Dieter Fox · 2021
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Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2021
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kPAM 2.0: Feedback control for category-level robotic manipulation
Wei Gao and Russ Tedrake · 2021
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Unsupervised learning of 3d object categories from videos in the wild
Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, and David Novotny · 2021
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Xu Chen, Zijian Dong, Jie Song, Andreas Geiger, and Otmar Hilliges · 2020
Cited alongside, same era.
A probabilistic framework for constrained manipulations and task and motion planning under uncertainty
Jung-Su Ha, Danny Driess, and Marc Toussaint · 2020
Cited alongside, same era.
Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning
Lucas Manuelli, Yunzhu Li, Pete Florence, and Russ Tedrake · 2020
Cited alongside, same era.
NeRF: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
Cited alongside, same era.
6-dof grasping for target-driven object manipulation in clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, and Dieter Fox · 2020
Cited alongside, same era.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Cited alongside, same era.
LatentFusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation
Keunhong Park, Arsalan Mousavian, Yu Xiang, and Dieter Fox · 2020
Cited alongside, same era.
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Synergies between affordance and geometry: 6-dof grasp detection via implicit representations
Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, and Yuke Zhu · 2021
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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 2021
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Neural descriptor fields: Se(3)-equivariant object representations for manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann · 2021
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Contact-GraspNet: Efficient 6-dof grasp generation in cluttered scenes
Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, and Dieter Fox · 2021
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GRF: Learning a general radiance field for 3d scene representation and rendering
Alex Trevithick and Bo Yang · 2021
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GIFT: Generalizable interaction-aware functional tool affordances without labels
Dylan Turpin, Liquan Wang, Stavros Tsogkas, Sven Dickinson, and Animesh Garg · 2021
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iNeRF: Inverting neural radiance fields for pose estimation
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin · 2021
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OmniHang: Learning to hang arbitrary objects using contact point correspondences and neural collision estimation
Yifan You, Lin Shao, Toki Migimatsu, and Jeannette Bohg · 2021
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pixelNeRF: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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SORNet: Spatial object-centric representations for sequential manipulation
Wentao Yuan, Chris Paxton, Karthik Desingh, and Dieter Fox · 2021
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Learning multi-object dynamics with compositional neural radiance fields
Danny Driess, Zhiao Huang, Yunzhu Li, Russ Tedrake, and Marc Toussaint · 2022
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