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Deep reinforcement learning has shown promising results on an abundance of robotic tasks in simulation, including visual navigation and manipulation.
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Oussama Khatib · 1999
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Steven M LaValle and James J Kuffner Jr · 2001
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Lyapunov design for safe reinforcement learning
Theodore J. Perkins and Andrew G. Barto · 2002
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Mike Stilman and James J Kuffner · 2005
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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CHOMP: gradient optimization techniques for efficient motion planning
Nathan D. Ratliff, Matthew Zucker, J. Andrew Bagnell, and Siddhartha S. Srinivasa · 2009
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Deep auto-encoder neural networks in reinforcement learning
Sascha Lange and Martin A. Riedmiller · 2010
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Combined task and motion planning for mobile manipulation
Jason Andrew Wolfe, Bhaskara Marthi, and Stuart J. Russell · 2010
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Visual navigation with obstacle avoidance
Andrea Cherubini and François Chaumette · 2011
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A framework for push-grasping in clutter
Mehmet Remzi Dogar and Siddhartha S. Srinivasa · 2011
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Depth space approach to human-robot collision avoidance
Fabrizio Flacco, Torsten Kröger, Alessandro De Luca, and Oussama Khatib · 2012
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Navigation in three-dimensional cluttered environments for mobile manipulation
Armin Hornung, Mike Phillips, Edward Gil Jones, Maren Bennewitz, Maxim Likhachev, and Sachin Chitta · 2012
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Unifying perception, estimation and action for mobile manipulation via belief space planning
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2012
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Learning and reasoning with action-related places for robust mobile manipulation
Freek Stulp, Andreas Fedrizzi, Lorenz Mösenlechner, and Michael Beetz · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
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Finding locally optimal, collision-free trajectories with sequential convex optimization
John Schulman, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, and Pieter Abbeel · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Introspective perception: Learning to predict failures in vision systems
Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, and Martial Hebert · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Multi-leapmotion sensor based demonstration for robotic refine tabletop object manipulation task
Haiyang Jin, Qing Chen, Zhixian Chen, Ying Hu, and Jianwei Zhang · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Grasping
Domenico Prattichizzo and Jeffrey C Trinkle · 2016
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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu · 2017
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 Robert Hogan, Maria Bauzá, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, Nima Fazeli, Ferran Alet, Nikhil Chavan Dafle, Rachel Holladay, Isabella Morona, Prem Qu Nair, Druck Green, Ian J. Taylor, Weber Liu, Thomas A. Funkhouser, and Alberto Rodriguez · 2018
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Shaping belief states with generative environment models for RL
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, and Aäron van den Oord · 2019
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HRL4IN: hierarchical reinforcement learning for interactive navigation with mobile manipulators
Chengshu Li, Fei Xia, Roberto Martín-Martín, and Silvio Savarese · 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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Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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PLATO: policy learning using adaptive trajectory optimization
Gregory Kahn, Tianhao Zhang, Sergey Levine, and Pieter Abbeel · 2017
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AI2-THOR: an interactive 3d environment for visual AI
Eric Kolve, Roozbeh Mottaghi, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi · 2017
Cited alongside, same era.
Playing FPS games with deep reinforcement learning
Guillaume Lample and Devendra Singh Chaplot · 2017
Cited alongside, same era.
Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator
Zhijun Li, Ting Zhao, Fei Chen, Yingbai Hu, Chun-Yi Su, and Toshio Fukuda · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross B. Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
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Habitat: A platform for embodied AI research
Manolis Savva, Jitendra Malik, Devi Parikh, Dhruv Batra, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, and Vladlen Koltun · 2019
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Reward constrained policy optimization
Chen Tessler, Daniel J. Mankowitz, and Shie Mannor · 2019
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Embodied question answering in photorealistic environments with point cloud perception
Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra · 2019
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Neural topological SLAM for visual navigation
Devendra Singh Chaplot, Ruslan Salakhutdinov, Abhinav Gupta, and Saurabh Gupta · 2020
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Robothor: An open simulation-to-real embodied AI platform
Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, and Ali Farhadi · 2020
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Threedworld: A platform for interactive multi-modal physical simulation
Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J. DiCarlo, Josh H. McDermott, Joshua B. Tenenbaum, and Daniel L. K. Yamins · 2020
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Bootstrap latent-predictive representations for multitask reinforcement learning
Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché, Rémi Munos, and Mohammad Gheshlaghi Azar · 2020
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Learning to walk in the real world with minimal human effort
Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, and Jie Tan · 2020
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Rlbench: The robot learning benchmark and learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J. Davison · 2020
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Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning
Ryan Julian, Benjamin Swanson, Gaurav Sukhatme, Sergey Levine, Chelsea Finn, and Karol Hausman · 2020
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CURL: contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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6-dof grasping for target-driven object manipulation in clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, and Dieter Fox · 2020
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Curriculum learning for reinforcement learning domains: A framework and survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E Taylor, and Peter Stone · 2020
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igibson, a simulation environment for interactive tasks in large realistic scenes
Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín, Linxi Fan, Guanzhi Wang, Shyamal Buch, Claudia D’Arpino, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent Vainio, Li Fei-Fei, and Silvio Savarese · 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 A. Funkhouser · 2020
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Learning mobile manipulation through deep reinforcement learning
Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan R. Petillot, and Sen Wang · 2020
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Allenact: A framework for embodied AI research
Luca Weihs, Jordi Salvador, Klemen Kotar, Unnat Jain, Kuo-Hao Zeng, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2020
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How to train pointgoal navigation agents on a (sample and compute) budget
Erik Wijmans, Irfan Essa, and Dhruv Batra · 2020
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DD-PPO: learning near-perfect pointgoal navigators from 2.5 billion frames
Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, and Dhruv Batra · 2020
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Spatial action maps for mobile manipulation
Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Johnny Lee, Szymon Rusinkiewicz, and Thomas A. Funkhouser · 2020
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SAPIEN: A simulated part-based interactive environment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, and Hao Su · 2020
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Auxiliary tasks speed up learning pointgoal navigation
Joel Ye, Dhruv Batra, Erik Wijmans, and Abhishek Das · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Manipulathor: A framework for visual object manipulation
Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, and Roozbeh Mottaghi · 2021
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Collision avoidance in pedestrian-rich environments with deep reinforcement learning
Michael Everett, Yu Fan Chen, and Jonathan P. How · 2021
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A collision avoidance method based on deep reinforcement learning
Shumin Feng, Bijo Sebastian, and Pinhas Ben-Tzvi · 2021
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BADGR: an autonomous self-supervised learning-based navigation system
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Articulated object interaction in unknown scenes with whole-body mobile manipulation
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Relmm: Practical RL for learning mobile manipulation skills using only onboard sensors
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Habitat 2.0: Training home assistants to rearrange their habitat
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Improving sample efficiency in model-free reinforcement learning from images
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