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This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems.
Space/time trade-offs in hash coding with allowable errors
Burton H Bloom · 1970
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Shakey the robot
Nils J Nilsson · 1984
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Learning policies for partially observable environments: Scaling up
Michael L Littman, Anthony R Cassandra, and Leslie P Kaelbling · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Vision for mobile robot navigation: A survey
Guilherme N DeSouza and Avinash C Kak · 2002
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Probabilistic Robotics
Sebastian Thrun, Wolfram Burgard, and Dieter Fox · 2005
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Stanley: The robot that won the DARPA Grand Challenge
Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, et al · 2006
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SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces
Hanna Kurniawati, David Hsu, and Wee Sun Lee · 2008
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PILCO: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
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LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
Jur Van Den Berg, Pieter Abbeel, and Ken Goldberg · 2011
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Reinforcement learning with limited reinforcement: Using bayes risk for active learning in pomdps
Finale Doshi-Velez, Joelle Pineau, and Nicholas Roy · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Dynamic programming
Richard Bellman · 2013
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Model regularization for stable sample rollouts
Erik Talvitie · 2014
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Real-world reinforcement learning via multifidelity simulators
Mark Cutler, Thomas J Walsh, and Jonathan P How · 2015
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The dependence of effective planning horizon on model accuracy
Nan Jiang, Alex Kulesza, Satinder Singh, and Richard Lewis · 2015
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Gradient estimation using stochastic computation graphs
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Backprop KF: Learning discriminative deterministic state estimators
Tuomas Haarnoja, Anurag Ajay, Sergey Levine, and Pieter Abbeel · 2016
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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 · 2016
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End-to-end learnable histogram filters
Rico Jonschkowski and Oliver Brock · 2016
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Differentiable MPC for end-to-end planning and control
Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots, and J Zico Kolter · 2018
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On evaluation of embodied navigation agents
Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, et al · 2018
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Learning dexterous in-hand manipulation
Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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A neural data structure for novelty detection
Sanjoy Dasgupta, Timothy C Sheehan, Charles F Stevens, and Saket Navlakha · 2018
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Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016
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Value iteration networks
Aviv Tamar, Sergey Levine, Pieter Abbeel, Yi Wu, and Garrett Thomas · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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Goal-driven dynamics learning via bayesian optimization
Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, and Claire J Tomiin · 2017
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Task-based end-to-end model learning in stochastic optimization
Priya Donti, Brandon Amos, and J Zico Kolter · 2017
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QMDP-net: Deep learning for planning under partial observability
Peter Karkus, David Hsu, and Wee Sun Lee · 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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Iterative value-aware model learning
Amir-Massoud Farahmand · 2018
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TreeQN and ATreeC: Differentiable tree planning for deep reinforcement learning
Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, and Shimon Whiteson · 2018
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Learning to search with MCTSnets
Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, and David Silver · 2018
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Residual reinforcement learning for robot control
Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, and Sergey Levine · 2018
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Differentiable particle filters: End-to-end learning with algorithmic priors
Rico Jonschkowski, Divyam Rastogi, and Oliver Brock · 2018
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Particle filter networks with application to visual localization
Peter Karkus, David Hsu, and Wee Sun Lee · 2018
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MPC-inspired neural network policies for sequential decision making
Marcus Pereira, David D Fan, Gabriel Nakajima An, and Evangelos Theodorou · 2018
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Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
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Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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On learning heteroscedastic noise models within differentiable bayes filters, 2019
Alina Kloss and Jeannette Bohg · 2019
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Credit assignment techniques in stochastic computation graphs
Théophane Weber, Nicolas Heess, Lars Buesing, and David Silver · 2019
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