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We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments, where users may have systematic biases that lead to suboptimal behavior: they might struggle to process observations from multiple sensors simultaneously, receive delayed observations, or overestimate distances to obstacles.
Making the world differentiable: On using self-supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments
J. Schmidhuber · 1990
Earlier work this paper cites.
Q-learning
C. J. Watkins and P. Dayan · 1992
Earlier work this paper cites.
Perception as Bayesian inference
D. C. Knill and W. Richards · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
L. P. Kaelbling, M. L. Littman, and A. R. Cassandra · 1998
Earlier work this paper cites.
The MNIST database of handwritten digits, 1998
Y. LeCun · 1998
Earlier work this paper cites.
Cybernetics of tunnel-in-the-sky displays
M. Mulder · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
A. Y. Ng and S. J. Russell · 2000
Earlier work this paper cites.
Probalistic robotics
S. Thrun, W. Burgard, and D. Fox · 2005
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
Action understanding as inverse planning
C. L. Baker, R. Saxe, and J. B. Tenenbaum · 2009
Earlier work this paper cites.
Listen to it yourself! evaluating usability of what’s around me? for the blind
S. A. Panëels, A. Olmos, J. R. Blum, and J. R. Cooperstock · 2013
Earlier work this paper cites.
Space telerobotics: unique challenges to human-robot collaboration in space
T. Fong, J. Rochlis Zumbado, N. Currie, A. Mishkin, and D. L. Akin · 2013
Earlier work this paper cites.
Uncovering information needs for independent spatial learning for users who are visually impaired
N. Banovic, R. L. Franz, K. N. Truong, J. Mankoff, and A. K. Dey · 2013
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
Infinite time horizon maximum causal entropy inverse reinforcement learning
M. Bloem and N. Bambos · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Learning the preferences of ignorant, inconsistent agents
O. Evans, A. Stuhlmüller, and N. Goodman · 2016
Cited alongside, same era.
Where do you think you’re going?: Inferring beliefs about dynamics from behavior
S. Reddy, A. D. Dragan, and S. Levine · 2018
Later among the works it cites.
Personalized dynamics models for adaptive assistive navigation systems
E. Ohn-Bar, K. Kitani, and C. Asakawa · 2018
Later among the works it cites.
Recurrent world models facilitate policy evolution
D. Ha and J. Schmidhuber · 2018
Later among the works it cites.
Designing and evaluating a customizable head-mounted vision enhancement system for people with low vision
Y. Zhao, S. Szpiro, L. Shi, and S. Azenkot · 2019
Later among the works it cites.
Inverse rational control with partially observable continuous nonlinear dynamics
S. Daptardar, P. Schrater, and X. Pitkow · 2019
Later among the works it cites.
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Risk-sensitive inverse reinforcement learning via coherent risk models
A. Majumdar, S. Singh, A. Mandlekar, and M. Pavone · 2017
Cited alongside, same era.
I see what you see: Inferring sensor and policy models of human real-world motor behavior
F. Schmitt, H.-J. Bieg, M. Herman, and C. A. Rothkopf · 2017
Cited alongside, same era.
Enabling independent navigation for visually impaired people through a wearable vision-based feedback system
H.-C. Wang, R. K. Katzschmann, S. Teng, B. Araki, L. Giarré, and D. Rus · 2017
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Navcog3: An evaluation of a smartphone-based blind indoor navigation assistant with semantic features in a large-scale environment
D. Sato, U. Oh, K. Naito, H. Takagi, K. Kitani, and C. Asakawa · 2017
Cited alongside, same era.
Virtual navigation for blind people: Building sequential representations of the real-world
J. Guerreiro, D. Ahmetovic, K. M. Kitani, and C. Asakawa · 2017
Cited alongside, same era.
Matterport3d: Learning from rgb-d data in indoor environments
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang · 2017
Cited alongside, same era.
S. Hilgard, N. Rosenfeld, M. R. Banaji, J. Cao, and D. C. Parkes · 2019
Later among the works it cites.
SOLAR: Deep structured representations for model-based reinforcement learning
M. Zhang, S. Vikram, L. Smith, P. Abbeel, M. Johnson, and S. Levine · 2019
Later among the works it cites.
Dream to control: Learning behaviors by latent imagination
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi · 2019
Later among the works it cites.
Habitat: A Platform for Embodied AI Research
Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra · 2019
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Inverse active sensing: Modeling and understanding timely decision-making
D. Jarrett and M. van der Schaar · 2020
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Theory of mind based communication for human agent cooperation
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Sequential explanations with mental model-based policies
A. Y. Yeung, S. Joshi, J. J. Williams, and F. Rudzicz · 2020
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