Fetching the paper…
Reading the bibliography…
A significant challenge for real-world robotic manipulation is the effective 6DoF grasping of objects in cluttered scenes from any single viewpoint without the need for additional scene exploration.
The theory of affordances
James J Gibson · 1977
Earlier work this paper cites.
Benchmarking in manipulation research: The ycb object and model set and benchmarking protocols
Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar · 2015
Earlier work this paper cites.
High precision grasp pose detection in dense clutter
Marcus Gualtieri, Andreas ten Pas, Kate Saenko, and Robert Platt Jr · 2016
Earlier work this paper cites.
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Earlier work this paper cites.
Grasp pose detection in point clouds
Andreas ten Pas, Marcus Gualtieri, Kate Saenko, and Robert Platt Jr · 2017
Earlier work this paper cites.
Dex-net 3.0: Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning
Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, and Ken Goldberg · 2018
Earlier work this paper cites.
Learning grasp affordance reasoning through semantic relations
Paola Ardón, Eric Pairet, Ronald PA Petrick, Subramanian Ramamoorthy, and Katrin S Lohan · 2019
Earlier work this paper cites.
Pointnetgpd: Detecting grasp configurations from point sets
Hongzhuo Liang, Xiaojian Ma, Shuang Li, Michael Görner, Song Tang, Bin Fang, Fuchun Sun, and Jianwei Zhang · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su · 2019
Earlier work this paper cites.
6-dof graspnet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
Earlier work this paper cites.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove · 2019
Earlier work this paper cites.
Keto: Learning keypoint representations for tool manipulation, 2019
Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei, and Silvio Savarese · 2019
Earlier work this paper cites.
Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
Earlier work this paper cites.
Volumetric grasping network: Real-time 6 DOF grasp detection in clutter
Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, and Juan I. Nieto · 2020
Earlier work this paper cites.
Orientation attentive robotic grasp synthesis with augmented grasp map representation
Georgia Chalvatzaki, Nikolaos Gkanatsios, Petros Maragos, and Jan Peters · 2020
Earlier work this paper cites.
Learning task-oriented grasping from human activity datasets
Mia Kokic, Danica Kragic, and Jeannette Bohg · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation
D. Morrison, P. Corke, and J. Leitner · 2020
Earlier work this paper cites.
Learning robust, real-time, reactive robotic grasping
Douglas Morrison, Peter Corke, and Jürgen Leitner · 2020
Earlier work this paper cites.
Same object, different grasps: Data and semantic knowledge for task-oriented grasping
Adithyavairavan Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, and Abhinav Gupta · 2020
Cited alongside, same era.
Pointnet++ grasping: Learning an end-to-end spatial grasp generation algorithm from sparse point clouds
Peiyuan Ni, Wenguang Zhang, Xiaoxiao Zhu, and Qixin Cao · 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.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars M. Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Cited alongside, same era.
Grasp proposal networks: An end-to-end solution for visual learning of robotic grasps
Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, and Kui Jia · 2020
Cited alongside, same era.
Anygrasp: Robust and efficient grasp perception in spatial and temporal domains
Hao-Shu Fang, Chenxi Wang, Hongjie Fang, Minghao Gou, Jirong Liu, Hengxu Yan, Wenhai Liu, Yichen Xie, and Cewu Lu · 2022
Later among the works it cites.
Context-aware grasp generation in cluttered scenes
Dinh-Cuong Hoang, Johannes A Stork, and Todor Stoyanov · 2022
Later among the works it cites.
Robot learning of mobile manipulation with reachability behavior priors
Snehal Jauhri, Jan Peters, and Georgia Chalvatzaki · 2022
Later among the works it cites.
Deep learning approaches to grasp synthesis: A review
Rhys Newbury, Morris Gu, Lachlan Chumbley, Arsalan Mousavian, Clemens Eppner, Jürgen Leitner, Jeannette Bohg, Antonio Morales, Tamim Asfour, Danica Kragic, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2021
Cited alongside, same era.
Rgb matters: Learning 7-dof grasp poses on monocular rgbd images
Minghao Gou, Hao-Shu Fang, Zhanda Zhu, Sheng Xu, Chenxi Wang, and Cewu Lu · 2021
Cited alongside, same era.
Gdn: A coarse-to-fine (c2f) representation for end-to-end 6-dof grasp detection
Kuang-Yu Jeng, Yueh-Cheng Liu, Zhe Yu Liu, Jen-Wei Wang, Ya-Liang Chang, Hung-Ting Su, and Winston Hsu · 2021
Cited alongside, same era.
Synergies between affordance and geometry: 6-dof grasp detection via implicit representations
Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, and Yuke Zhu · 2021
Cited alongside, same era.
A review of robot learning for manipulation: Challenges, representations, and algorithms
Oliver Kroemer, Scott Niekum, and George Konidaris · 2021
Cited alongside, same era.
Ddgc: Generative deep dexterous grasping in clutter
Jens Lundell, Francesco Verdoja, and Ville Kyrki · 2021
Cited alongside, same era.
UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
Cited alongside, same era.
Julen Urain, Niklas Funk, Georgia Chalvatzaki, and Jan Peters · 2022
Later among the works it cites.
Neural grasp distance fields for robot manipulation
Thomas Weng, David Held, Franziska Meier, and Mustafa Mukadam · 2022
Later among the works it cites.
Affordances from human videos as a versatile representation for robotics
Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, and Deepak Pathak · 2023
Closest in time.
Yiye Chen, Ruinian Xu, Yunzhi Lin, and Patricio A Vela · 2023
Closest in time.
Eugenio Chisari, Nick Heppert, Tim Welschehold, Wolfram Burgard, and Abhinav Valada · 2023
Closest in time.
Causal policy gradient for whole-body mobile manipulation
Jiaheng Hu, Peter Stone, and Roberto Martín-Martín · 2023
Closest in time.
Edge grasp network: A graph-based se(3)-invariant approach to grasp detection
Haojie Huang, Dian Wang, Xupeng Zhu, Robin Walters, and Robert Platt · 2023
Closest in time.
Neuralgrasps: Learning implicit representations for grasps of multiple robotic hands
Ninad Khargonkar, Neil Song, Zesheng Xu, Balakrishnan Prabhakaran, and Yu Xiang · 2023
Closest in time.
Contact2grasp: 3d grasp synthesis via hand-object contact constraint, 2023
Haoming Li, Xinzhuo Lin, Yang Zhou, Xiang Li, Yuchi Huo, Jiming Chen, and Qi Ye · 2023
Closest in time.
Constrained generative sampling of 6-dof grasps
Jens Lundell, Francesco Verdoja, Tran Nguyen Le, Arsalan Mousavian, Dieter Fox, and Ville Kyrki · 2023
Closest in time.
3dsgrasp: 3d shape-completion for robotic grasp
Seyed S Mohammadi, Nuno F Duarte, Dimitris Dimou, Yiming Wang, Matteo Taiana, Pietro Morerio, Atabak Dehban, Plinio Moreno, Alexandre Bernardino, Alessio Del Bue, et al · 2023
Closest in time.
Grasp learning: Models, methods, and performance
Robert Platt · 2023
Closest in time.
Language embedded radiance fields for zero-shot task-oriented grasping
Satvik Sharma, Adam Rashid, Chung Min Kim, Justin Kerr, Lawrence Yunliang Chen, Angjoo Kanazawa, and Ken Goldberg · 2023
Closest in time.
Distilled feature fields enable few-shot language-guided manipulation
William Shen, Ge Yang, Alan Yu, Jansen Wong, Leslie Pack Kaelbling, and Phillip Isola · 2023
Closest in time.
Alto: Alternating latent topologies for implicit 3d reconstruction
Zhen Wang, Shijie Zhou, Jeong Joon Park, Despoina Paschalidou, Suya You, Gordon Wetzstein, Leonidas Guibas, and Achuta Kadambi · 2023
Closest in time.
Learning continuous grasping function with a dexterous hand from human demonstrations
Jianglong Ye, Jiashun Wang, Binghao Huang, Yuzhe Qin, and Xiaolong Wang · 2023
Closest in time.