Fetching the paper…
Reading the bibliography…
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Rational choice and the structure of the environment
H.A. Simon · 1956
Earlier work this paper cites.
Learning and executing generalized robot plans
Richard E Fikes, Peter E Hart, and Nils J Nilsson · 1972
Earlier work this paper cites.
Mind in Society: the development of higher mental processes
L. S. Vygotsky · 1978
Earlier work this paper cites.
Metaphors We Live By (Chicago, IL: U. of Chicago P.)
G Lakoff and M Johnson · 1980
Earlier work this paper cites.
The need for biases in learning generalizations
Tom M Mitchell · 1980
Earlier work this paper cites.
Impedance control: An approach to manipulation: Part 1 – theory
N. Hogan · 1985
Earlier work this paper cites.
Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
Earlier work this paper cites.
The organization of learning
Charles R Gallistel · 1990
Earlier work this paper cites.
The symbol grounding problem
Stevan Harnad · 1990
Earlier work this paper cites.
What determines initial feeling of knowing? Familiarity with question terms, not with the answer
Lynne M Reder and Frank E Ritter · 1992
Earlier work this paper cites.
A cognitive theory of consciousness
Bernard J Baars · 1993
Earlier work this paper cites.
Real robots, real learning problems
Rodney A. Brooks and Maja J. Mataric · 1993
Earlier work this paper cites.
Signal-to-symbol transformation and vice versa: From fundamental processes to representation
Ruzena Bajcsy · 1995
Earlier work this paper cites.
Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
Earlier work this paper cites.
The sciences of the artificial
Herbert A. Simon · 1996
Earlier work this paper cites.
In the theatre of consciousness. global workspace theory, a rigorous scientific theory of consciousness
Bernard J Baars · 1997
Earlier work this paper cites.
‘Improving ratings’: Audit in the British University system
Marilyn Strathern · 1997
Earlier work this paper cites.
No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
Earlier work this paper cites.
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
Earlier work this paper cites.
Hierarchical reinforcement learning with the MAXQ value function decomposition
Thomas G Dietterich · 2000
Earlier work this paper cites.
The supervised learning no-free-lunch theorems
David H Wolpert · 2002
Earlier work this paper cites.
Metacognition in computation: A selected research review
Michael T Cox · 2005
Earlier work this paper cites.
Searching for common sense: Populating CYC from the web
Cynthia Matuszek, Michael Witbrock, Robert C Kahlert, John Cabral, Dave Schneider, Purvesh Shah, and Doug Lenat · 2005
Earlier work this paper cites.
How the body shapes the way we think: a new view of intelligence
Rolf Pfeifer and Josh Bongard · 2006
Earlier work this paper cites.
Learning symbolic models of stochastic domains
Hanna M Pasula, Luke S Zettlemoyer, and Leslie Pack Kaelbling · 2007
Earlier work this paper cites.
The embodied cognition research programme
Larry Shapiro · 2007
Earlier work this paper cites.
Core knowledge
Elizabeth S Spelke and Katherine D Kinzler · 2007
Earlier work this paper cites.
A fully automated framework for control of linear systems from temporal logic specifications
Marius Kloetzer and Calin Belta · 2008
Earlier work this paper cites.
An experiment in robot discovery with ilp
Gregor Leban, Jure Žabkar, and Ivan Bratko · 2008
Earlier work this paper cites.
Temporal-logic-based reactive mission and motion planning
Hadas Kress-Gazit, Georgios E Fainekos, and George J Pappas · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Comparison of machine learning for autonomous robot discovery
Ivan Bratko · 2010
Earlier work this paper cites.
Algorithms for active learning
Daniel Joseph Hsu · 2010
Earlier work this paper cites.
Animal tool-use
Amanda Seed and Richard Byrne · 2010
Earlier work this paper cites.
Towards semantic slam using a monocular camera
Javier Civera, Dorian Gálvez-López, Luis Riazuelo, Juan D Tardós, and JMM Montiel · 2011
Earlier work this paper cites.
Thinking Fast and Slow
Daniel Kahneman · 2011
Earlier work this paper cites.
Object–action complexes: Grounded abstractions of sensory–motor processes
Norbert Krüger, Christopher Geib, Justus Piater, Ronald Petrick, Mark Steedman, Florentin Wörgötter, Aleš Ude, Tamim Asfour, Dirk Kraft, Damir Omrčen, Alejandro Agostini, and Rüdiger Dillmann · 2011
Earlier work this paper cites.
Knows what it knows: a framework for self-aware learning
Lihong Li, Michael L Littman, Thomas J Walsh, and Alexander L Strehl · 2011
Earlier work this paper cites.
Autonomous learning of high-level states and actions in continuous environments
Jonathan Mugan and Benjamin Kuipers · 2011
Earlier work this paper cites.
Skill learning and task outcome prediction for manipulation
Peter Pastor, Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, and Stefan Schaal · 2011
Earlier work this paper cites.
Approaching the symbol grounding problem with probabilistic graphical models
Stefanie Tellex, Thomas Kollar, Steven Dickerson, Matthew R Walter, Ashis Gopal Banerjee, Seth Teller, and Nicholas Roy · 2011
Earlier work this paper cites.
Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
Earlier work this paper cites.
A short review of symbol grounding in robotic and intelligent systems
Silvia Coradeschi, Amy Loutfi, and Britta Wrede · 2013
Earlier work this paper cites.
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
A flexible and robust large scale capacitive tactile system for robots
Perla Maiolino, Marco Maggiali, Giorgio Cannata, Giorgio Metta, and Lorenzo Natale · 2013
Earlier work this paper cites.
Learning to parse natural language commands to a robot control system
Cynthia Matuszek, Evan Herbst, Luke Zettlemoyer, and Dieter Fox · 2013
Earlier work this paper cites.
Reconciling intuitive physics and Newtonian mechanics for colliding objects
Adam N Sanborn, Vikash K Mansinghka, and Thomas L Griffiths · 2013
Earlier work this paper cites.
Semantic localization via the matrix permanent
Nikolay Atanasov, Menglong Zhu, Kostas Daniilidis, and George J Pappas · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Constructing symbolic representations for high-level planning
George Konidaris, Leslie Kaelbling, and Tomas Lozano-Perez · 2014
Earlier work this paper cites.
Localization and manipulation of small parts using GelSight tactile sensing
Rui Li, Robert Platt, Wenzhen Yuan, Andreas ten Pas, Nathan Roscup, Mandayam A Srinivasan, and Edward Adelson · 2014
Earlier work this paper cites.
PR2 Looking at Things: Ensemble Learning for Unstructured Information Processing with Markov Logic Networks
Daniel Nyga, Ferenc Balint-Benczedi, and Michael Beetz · 2014
Earlier work this paper cites.
A PAC-Bayesian bound for lifelong learning
Anastasia Pentina and Christoph Lampert · 2014
Earlier work this paper cites.
An autonomous manipulation system based on force control and optimization
Ludovic Righetti, Mrinal Kalakrishnan, Peter Pastor, Jonathan Binney, Jonathan Kelly, Randolph C. Voorhies, Gaurav S. Sukhatme, and Stefan Schaal · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Relational kernel-based grasping with numerical features
Laura Antanas, Plinio Moreno, and Luc De Raedt · 2015
Earlier work this paper cites.
No falls, no resets: Reliable humanoid behavior in the DARPA robotics challenge
C. G. Atkeson, B. P. W. Babu, N. Banerjee, D. Berenson, C. P. Bove, X. Cui, M. DeDonato, R. Du, S. Feng, P. Franklin, M. Gennert, J. P. Graff, P. He, A. Jaeger, J. Kim, K. Knoedler, L. Li, C. Liu, X. Long, T. Padir, F. Polido, G. G. Tighe, and X. Xinjilefu · 2015
Earlier work this paper cites.
Embodied intelligence
Angelo Cangelosi, Josh Bongard, Martin H Fischer, and Stefano Nolfi · 2015
Earlier work this paper cites.
Leveraging big data for grasp planning
Daniel Kappler, Jeannette Bohg, and Stefan Schaal · 2015
Earlier work this paper cites.
Picture: A probabilistic programming language for scene perception
Tejas D Kulkarni, Pushmeet Kohli, Joshua B Tenenbaum, and Vikash Mansinghka · 2015
Earlier work this paper cites.
Never-ending learning
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling · 2015
Earlier work this paper cites.
Reactive synthesis from signal temporal logic specifications
Vasumathi Raman, Alexandre Donzé, Dorsa Sadigh, Richard M Murray, and Sanjit A Seshia · 2015
Earlier work this paper cites.
The robot engineer
Claude Sammut, Raymond Sheh, Adam Haber, and Handy Wicaksono · 2015
Cited alongside, same era.
Learning to interpret natural language commands through human-robot dialog
Jesse Thomason, Shiqi Zhang, Raymond J Mooney, and Peter Stone · 2015
Cited alongside, same era.
Logic-geometric programming: An optimization-based approach to combined task and motion planning
Marc Toussaint · 2015
Cited alongside, same era.
A soft version of predicate invention based on structured sparsity
William Yang Wang, Kathryn Mazaitis, and William W Cohen · 2015
Cited alongside, same era.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Cited alongside, same era.
Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Symbol emergence in cognitive developmental systems: a survey
Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann, Lorenzo Jamone, Takayuki Nagai, Benjamin Rosman, Toshihiko Matsuka, Naoto Iwahashi, Erhan Oztop, Justus Piater, and Florentin Wörgötter · 2018
Later among the works it cites.
Differentiable physics and stable modes for tool-use and manipulation planning
Marc Toussaint, Kelsey R Allen, Kevin A Smith, and Josh B Tenenbaum · 2018
Later among the works it cites.
Sensor-based reactive symbolic planning in partially known environments
Vasileios Vasilopoulos, William Vega-Brown, Omur Arslan, Nicholas Roy, and Daniel Koditschek · 2018
Later among the works it cites.
TreeQN and ATreeC: Differentiable tree planning for deep reinforcement learning
Shimon Whiteson · 2018
Later among the works it cites.
Neural relational inference with fast modular meta-learning
Ferran Alet, Erica Weng, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
On the performance of the Baxter research robot
Sven Cremer, Lawrence Mastromoro, and Dan O Popa · 2016
Cited alongside, same era.
Introspective perception: Learning to predict failures in vision systems
Shreyansh Daftry, Sam Zeng, J Andrew Bagnell, and Martial Hebert · 2016
Cited alongside, same era.
Off the beaten track: Predicting localisation performance in visual teach and repeat
Julie Dequaire, Chi Hay Tong, Winston Churchill, and Ingmar Posner · 2016
Cited alongside, same era.
Metacognition in multisensory perception
Ophelia Deroy, Charles Spence, and Uta Noppeney · 2016
Cited alongside, same era.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S.M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, and Geoffrey E Hinton · 2016
Cited alongside, same era.
3D scene graph: A structure for unified semantics, 3D space, and camera
Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R Zamir, Martin Fischer, Jitendra Malik, and Silvio Savarese · 2019
Later among the works it cites.
Unsupervised grounding of plannable first-order logic representation from images
Masataro Asai · 2019
Later among the works it cites.
Option discovery using deep skill chaining
Akhil Bagaria and George Konidaris · 2019
Later among the works it cites.
Reinforcement learning, fast and slow
Mathew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis · 2019
Later among the works it cites.
MONet: Unsupervised Scene Decomposition and Representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
Later among the works it cites.
nuScenes: a multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
Later among the works it cites.
Argoverse: 3D tracking and forecasting with rich maps
Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, et al · 2019
Later among the works it cites.
Gen: a general-purpose probabilistic programming system with programmable inference
Marco F Cusumano-Towner, Feras A Saad, Alexander K Lew, and Vikash K Mansinghka · 2019
Later among the works it cites.
Neural logic machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, and Denny Zhou · 2019
Later among the works it cites.
Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
Later among the works it cites.
Recurrent independent mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann, Shagun Sodhani, Sergey Levine, Yoshua Bengio, and Bernhard Schölkopf · 2019
Later among the works it cites.
Multi-Object Representation Learning with Iterative Variational Inference
Klaus Greff, Raphaël Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Later among the works it cites.
Evolving embodied intelligence from materials to machines
David Howard, Agoston E. Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, and Dave Winkler · 2019
Later among the works it cites.
The compositionality of neural networks: integrating symbolism and connectionism
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2019
Later among the works it cites.
Reasoning about physical interactions with object-oriented prediction and planning
Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, and Jiajun Wu · 2019
Later among the works it cites.
Differentiable algorithm networks for composable robot learning
Peter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, and Tomas Lozano-Perez · 2019
Later among the works it cites.
A review and comparison of ontology-based approaches to robot autonomy
Alberto Olivares-Alarcos, Daniel Beßler, Alaa Khamis, Paulo Gonçalves, Maki Habib, J. Bermejo, Marcos Barreto, Mohammed Diab, Jan Rosell, João Quintas, Joanna Olszewska, Hirenkumar Nakawala, Edison Pignaton de Freitas, Amelie Gyrard, Stefano Borgo, Guillem Alenyà, Michael Beetz, and Howard Li · 2019
Later among the works it cites.
Inferring compact representations for efficient natural language understanding of robot instructions
Siddharth Patki, Andrea F Daniele, Matthew R Walter, and Thomas M Howard · 2019
Later among the works it cites.
GMNN: graph Markov neural networks
Meng Qu, Yoshua Bengio, and Jian Tang · 2019
Later among the works it cites.
BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
Fabio Ramos, Rafael Carvalhaes Possas, and Dieter Fox · 2019
Later among the works it cites.
Attacking optical flow
Anurag Ranjan, Joel Janai, Andreas Geiger, and Michael J. Black · 2019
Later among the works it cites.
Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
Later among the works it cites.
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, Devi Parikh, and Dhruv Batra · 2019
Later among the works it cites.
Scalability in perception for autonomous driving: Waymo open dataset, 2019
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov · 2019
Later among the works it cites.
Exploiting hierarchy for learning and transfer in kl-regularized rl
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, and Nicolas Heess · 2019
Later among the works it cites.
SATnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter · 2019
Later among the works it cites.
Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
Later among the works it cites.
Perspectives on deep multimodel robot learning
Wolfram Burgard, Abhinav Valada, Noha Radwan, Tayyab Naseer, Jingwei Zhang, Johan Vertens, Oier Mees, Andreas Eitel, and Gabriel Oliveira · 2020
Later among the works it cites.
A survey of algorithms for black-box safety validation
Anthony Corso, Robert J. Moss, Mark Koren, Ritchie Lee, and Mykel J. Kochenderfer · 2020
Later among the works it cites.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2020
Later among the works it cites.
Inductive biases for deep learning of higher-level cognition
Anirudh Goyal and Yoshua Bengio · 2020
Later among the works it cites.
2nd workshop on closing the reality gap in sim2real transfer for robotics, 2020
Sebastian Hofer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, and Melissa Mozifian · 2020
Later among the works it cites.
SCALOR: Generative World Models with Scalable Object Representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2020
Later among the works it cites.
The foundation of efficient robot learning
Leslie Pack Kaelbling · 2020
Later among the works it cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Later among the works it cites.
Learning robust, real-time, reactive robotic grasping
Douglas Morrison, Peter Corke, and Jürgen Leitner · 2020
Later among the works it cites.
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, and Niloy Mitra · 2020
Later among the works it cites.
Differentiable Neural Logic Networks And Their Application Onto Inductive Logic Programming
Ali Payani · 2020
Later among the works it cites.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
Later among the works it cites.
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes, 2020
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
Later among the works it cites.
Neural bridge sampling for evaluating safety-critical autonomous systems
Aman Sinha, Matthew O’Kelly, Russ Tedrake, and John C. Duchi · 2020
Later among the works it cites.
Physically realizable adversarial examples for lidar object detection, 2020
James Tu, Mengye Ren, Siva Manivasagam, Ming Liang, Bin Yang, Richard Du, Frank Cheng, and Raquel Urtasun · 2020
Later among the works it cites.
Entity abstraction in visual model-based reinforcement learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua Tenenbaum, and Sergey Levine · 2020
Later among the works it cites.
Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, and Bernhard Schölkopf · 2020
Later among the works it cites.
Surfelgan: Synthesizing realistic sensor data for autonomous driving
Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean Rafferty, and Henrik Kretzschmar · 2020
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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 · 2020
Later among the works it cites.
Sim-to-real transfer in deep reinforcement learning for robotics: a survey
Wenshuai Zhao, Jorge Peña Queralta, and Tomi Westerlund · 2020
Later among the works it cites.
Active acoustic contact sensing for soft pneumatic actuators
Gabriel Zöller, Vincent Wall, and Oliver Brock · 2020
Later among the works it cites.
Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
Closest in time.
Surprisingly robust in-hand manipulation: An empirical study
Aditya Bhatt, Adrian Sieler, Steffen Puhlmann, and Oliver Brock · 2021
Closest in time.
Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans, and Stephen H Muggleton · 2021
Closest in time.
Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomas Lozano-Perez · 2021
Closest in time.
Deup: Direct epistemic uncertainty prediction
Moksh Jain, Salem Lahlou, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio · 2021
Closest in time.
Simgan: Hybrid simulator identification for domain adaptation via adversarial reinforcement learning
Yifeng Jiang, Tingnan Zhang, Daniel Ho, Yunfei Bai, C. Karen Liu, Sergey Levine, and Jie Tan · 2021
Closest in time.
What is robotics? why do we need it and how can we get it?
Daniel E. Koditschek · 2021
Closest in time.
Discrete-valued neural communication
Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, and Yoshua Bengio · 2021
Closest in time.
There and back again: Learning to simulate radar data for real-world applications
Rob Weston, Oiwi Parker Jones, and Ingmar Posner · 2021
Closest in time.