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Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from images of the current state (e.g., a bird's-eye view of a SLAM reconstruction).
Development of a vision system for an outdoor service robot to collect trash on streets
Yasuhiro Fuchikawa, Takeshi Nishida, Shuichi Kurogi, Takashi Kondo, Fujio Ohkawa, Toshinori Suehiro, Yasuhiro Watanabe, Yoshinori Kawamura, Masayuki Obata, Hidekazu Miyagawa, et al · 2005
Earlier work this paper cites.
Navigation among movable obstacles: Real-time reasoning in complex environments
Mike Stilman and James J Kuffner · 2005
Earlier work this paper cites.
Off-road obstacle avoidance through end-to-end learning
Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp, and Yann L Cun · 2006
Earlier work this paper cites.
Development of outdoor service robots
Takeshi Nishida, Yuji Takemura, Yasuhiro Fuchikawa, Shuichi Kurogi, Shuji Ito, Masayuki Obata, Norio Hiratsuka, Hidekazu Miyagawa, Yasuhiro Watanabe, Fumitaka Koga, et al · 2006
Earlier work this paper cites.
Manipulation planning among movable obstacles
Mike Stilman, Jan-Ullrich Schamburek, James Kuffner, and Tamim Asfour · 2007
Earlier work this paper cites.
Path planning among movable obstacles: a probabilistically complete approach
Jur Van Den Berg, Mike Stilman, James Kuffner, Ming Lin, and Dinesh Manocha · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Hierarchical decision theoretic planning for navigation among movable obstacles
Martin Levihn, Jonathan Scholz, and Mike Stilman · 2013
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Learning monocular reactive uav control in cluttered natural environments
Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J Andrew Bagnell, and Martial Hebert · 2013
Earlier work this paper cites.
Deep neural networks for object detection
Christian Szegedy, Alexander Toshev, and Dumitru Erhan · 2013
Earlier work this paper cites.
Planning with macro-actions in decentralized pomdps
Christopher Amato, George D Konidaris, and Leslie P. Kaelbling · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Playing doom with slam-augmented deep reinforcement learning
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N Siddharth, and Philip HS Torr · 2016
Earlier work this paper cites.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2017
Cited alongside, same era.
Intention-net: Integrating planning and deep learning for goal-directed autonomous navigation
Wei Gao, David Hsu, Wee Sun Lee, Shengmei Shen, and Karthikk Subramanian · 2017
Cited alongside, same era.
Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2017
Cited alongside, same era.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Cited alongside, same era.
Gibson env: Real-world perception for embodied agents
Fei Xia, Amir R Zamir, Zhiyang He, Alexander Sax, Jitendra Malik, and Silvio Savarese · 2018
Later among the works it cites.
Chalet: Cornell house agent learning environment
Claudia Yan, Dipendra Misra, Andrew Bennnett, Aaron Walsman, Yonatan Bisk, and Yoav Artzi · 2018
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Autotrans: an autonomous open world transportation system
Brayan S Zapata-Impata, Vikrant Shah, Hanumant Singh, and Robert Platt · 2018
Later among the works it cites.
Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
Later among the works it cites.
Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, et al · 2018
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Cited alongside, same era.
From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots
Mark Pfeiffer, Michael Schaeuble, Juan Nieto, Roland Siegwart, and Cesar Cadena · 2017
Cited alongside, same era.
Minos: Multimodal indoor simulator for navigation in complex environments
Manolis Savva, Angel X Chang, Alexey Dosovitskiy, Thomas Funkhouser, and Vladlen Koltun · 2017
Cited alongside, same era.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2017
Cited alongside, same era.
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton van den Hengel · 2018
Cited alongside, same era.
Deep reinforcement learning to acquire navigation skills for wheel-legged robots in complex environments
Xi Chen, Ali Ghadirzadeh, John Folkesson, Mårten Björkman, and Patric Jensfelt · 2018
Cited alongside, same era.
Later among the works it cites.
The right (angled) perspective: Improving the understanding of road scenes using boosted inverse perspective mapping
Tom Bruls, Horia Porav, Lars Kunze, and Paul Newman · 2019
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Learning exploration policies for navigation
Tao Chen, Saurabh Gupta, and Abhinav Gupta · 2019
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Ai2-thor: An interactive 3d environment for visual ai
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi · 2019
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Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al · 2019
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Learning Visual Affordances for Robotic Manipulation
Andy Zeng · 2019
Later among the works it cites.
Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2019
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Learning to move with affordance maps
William Qi, Ravi Teja Mullapudi, Saurabh Gupta, and Deva Ramanan · 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
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Learning to see before learning to act: Visual pre-training for manipulation
Lin Yen-Chen, Shuran Zeng, Andy Song, Phillip Isola, and Tsung-Yi Lin · 2020
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Form2fit: Learning shape priors for generalizable assembly from disassembly
Kevin Zakka, Andy Zeng, Johnny Lee, and Shuran Song · 2020
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