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Tool use requires reasoning about the fit between an object's affordances and the demands of a task.
The theory of affordances
James J Gibson · 1977
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The Ecological Approach to Visual Perception
JJ Gibson · 1979
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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The earth mover’s distance as a metric for image retrieval
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas · 2000
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Learning task constraints for robot grasping using graphical models
Dan Song, Kai Huebner, Ville Kyrki, and Danica Kragic · 2010
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Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task
Hao Dang and Peter K Allen · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Data-driven grasp synthesis—a survey
Jeannette Bohg, Antonio Morales, Tamim Asfour, and Danica Kragic · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Earth mover’s distances on discrete surfaces
Justin Solomon, Raif Rustamov, Leonidas Guibas, and Adrian Butscher · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Affordance detection of tool parts from geometric features
Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
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Self-supervised learning of tool affordances from 3D tool representation through parallel SOM mapping
T. Mar, V. Tikhanoff, G. Metta, and L. Natale · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Unsupervised learning of object keypoints for perception and control
Tejas D Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, and Volodymyr Mnih · 2019
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Self-Attention Graph Pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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DeepGCNs: Can GCNs Go as Deep as CNNs?
Guohao Li, Matthias Müller, Ali Thabet, and Bernard Ghanem · 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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On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks
Vishal Satish, Jeffrey Mahler, and Ken Goldberg · 2019
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Information theoretic MPC for model-based reinforcement learning
Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James M Rehg, Byron Boots, and Evangelos A Theodorou · 2017
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Affordancenet: An end-to-end deep learning approach for object affordance detection
Thanh-Toan Do, Anh Nguyen, and Ian Reid · 2018
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Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision
Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kuryenkov, Viraj Mehta, Li Fei-Fei, and Silvio Savarese · 2018
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Unsupervised learning of object landmarks through conditional image generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, and Andrea Vedaldi · 2018
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Discovery of latent 3d keypoints via end-to-end geometric reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan J Tompson, and Mohammad Norouzi · 2018
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Learning Embedding of 3D models with Quadric Loss
Nitin Agarwal, Sung-Eui Yoon, and M Gopi · 2019
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Lyceum: An efficient and scalable ecosystem for robot learning
Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, and Emanuel Todorov
Cited in the paper.
Learning Labeled Robot Affordance Models Using Simulations and Crowdsourcing
Adam Allevato, Mitch Pryor, Elaine Schaertl Short, and Andrea L Thomaz · 2020
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Learning task-oriented grasping for tool manipulation from simulated self-supervision
Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, and Silvio Savarese · 2020
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Cage: Context-aware grasping engine
Weiyu Liu, Angel Daruna, and Sonia Chernova · 2020
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KETO: Learning Keypoint Representations for Tool Manipulation
Z. Qin, K. Fang, Y. Zhu, L. Fei-Fei, and S. Savarese · 2020
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6-PACK: Category-level 6d pose tracker with anchor-based keypoints
Chen Wang, Roberto Martín-Martín, Danfei Xu, Jun Lv, Cewu Lu, Li Fei-Fei, Silvio Savarese, and Yuke Zhu · 2020
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Where2Act: From Pixels to Actions for Articulated 3D Objects
Kaichun Mo, Leonidas Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani · 2021
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