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Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning.
Judgment under uncertainty: Heuristics and biases
Amos Tversky and Daniel Kahneman · 1974
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Does the chimpanzee have a theory of mind?
David Premack and Guy Woodruff · 1978
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Naive beliefs in “sophisticated” subjects: Misconceptions about trajectories of objects
Alfonso Caramazza, Michael McCloskey, and Bert Green · 1981
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Understanding natural dynamics
Dennis R Proffitt and David L Gilden · 1989
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Procrastination and obedience
George A Akerlof · 1991
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Bounded rationality and organizational learning
Herbert A Simon · 1991
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A theory of the child’s theory of mind
Jerry A Fodor · 1992
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The theory theory
Alison Gopnik and Henry M Wellman · 1994
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An internal model for sensorimotor integration
Daniel M Wolpert, Zoubin Ghahramani, and Michael I Jordan · 1995
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Internal models for motor control and trajectory planning
Mitsuo Kawato · 1999
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Forward modeling allows feedback control for fast reaching movements
Michel Desmurget and Scott Grafton · 2000
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Forward models in visuomotor control
Biren Mehta and Stefan Schaal · 2002
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Optimality principles in sensorimotor control
Emanuel Todorov · 2004
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Probabilistic and randomized methods for design under uncertainty
Giuseppe Calafiore and Fabrizio Dabbene · 2006
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Bayesian inverse reinforcement learning
Deepak Ramachandran and Eyal Amir · 2007
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Action understanding as inverse planning
Chris L Baker, Rebecca Saxe, and Joshua B Tenenbaum · 2009
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Goal-directed decision making in prefrontal cortex: a computational framework
Matthew Botvinick and James An · 2009
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Planning-based prediction for pedestrians
Brian D Ziebart, Nathan Ratliff, Garratt Gallagher, Christoph Mertz, Kevin Peterson, J Andrew Bagnell, Martial Hebert, Anind K Dey, and Siddhartha Srinivasa · 2009
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Learning structured preferences
Leon Bergen, Owain Evans, and Joshua Tenenbaum · 2010
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Children’s intuitive physics
Friedrich Wilkening and Trix Cacchione · 2010
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Modeling interaction via the principle of maximum causal entropy
Brian D Ziebart, J Andrew Bagnell, and Anind K Dey · 2010
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Internal physics models guide probabilistic judgments about object dynamics
Jessica Hamrick, Peter Battaglia, and Joshua B Tenenbaum · 2011
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Apprenticeship learning using inverse reinforcement learning and gradient methods
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Learning the preferences of ignorant, inconsistent agents
Owain Evans, Andreas Stuhlmüller, and Noah D Goodman · 2016
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Inverse reinforcement learning with simultaneous estimation of rewards and dynamics
Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, and Wolfram Burgard · 2016
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Using inverse planning for personalized feedback
Anna N Rafferty, Rachel Jansen, and Thomas L Griffiths · 2016
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Impossibility of deducing preferences and rationality from human policy
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Gergely Neu and Csaba Szepesvári · 2012
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Advances in neuroprosthetic learning and control
Jose M Carmena · 2013
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Learning an internal dynamics model from control demonstration
Matthew Golub, Steven Chase, and M Yu Byron · 2013
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Constrained optimization and Lagrange multiplier methods
Dimitri P Bertsekas · 2014
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Infinite time horizon maximum causal entropy inverse reinforcement learning
Michael Bloem and Nicholas Bambos · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Time-inconsistent planning: a computational problem in behavioral economics
Jon Kleinberg and Sigal Oren · 2014
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Stuart Armstrong and Sören Mindermann · 2017
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Learning models for shared control of human-machine systems with unknown dynamics
Alexander Broad, TD Murphey, and Brenna Argall · 2017
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Planning, fast and slow: A framework for adaptive real-time safe trajectory planning
David Fridovich-Keil, Sylvia L Herbert, Jaime F Fisac, Sampada Deglurkar, and Claire J Tomlin · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Intuitive theories
Tobias Gerstenberg and Joshua B Tenenbaum · 2017
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
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Fastrack: a modular framework for fast and guaranteed safe motion planning
Sylvia L Herbert, Mo Chen, SooJean Han, Somil Bansal, Jaime F Fisac, and Claire J Tomlin · 2017
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Risk-sensitive inverse reinforcement learning via coherent risk models
Anirudha Majumdar, Sumeet Singh, Ajay Mandlekar, and Marco Pavone · 2017
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Autonomy infused teleoperation with application to brain computer interface controlled manipulation
Katharina Muelling, Arun Venkatraman, Jean-Sebastien Valois, John E Downey, Jeffrey Weiss, Shervin Javdani, Martial Hebert, Andrew B Schwartz, Jennifer L Collinger, and J Andrew Bagnell · 2017
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Parallel autonomy in automated vehicles: Safe motion generation with minimal intervention
Wilko Schwarting, Javier Alonso-Mora, Liam Pauli, Sertac Karaman, and Daniela Rus · 2017
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Model-free deep inverse reinforcement learning by logistic regression
Eiji Uchibe · 2017
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Shared autonomy via deep reinforcement learning
Siddharth Reddy, Sergey Levine, and Anca Dragan · 2018
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