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In this work, we investigate Active Vision Reinforcement Learning (ActiveVision-RL), where an embodied agent simultaneously learns action policy for the task while also controlling its visual observations in partially observable environments.
Five types of eye movement in the horizontal meridian plane of the field of regard
R. Dodge · 1903
Earlier work this paper cites.
Piaget’s theory, 1976
J. Piaget · 1976
Earlier work this paper cites.
Reliability of the fugl-meyer assessment of sensorimotor recovery following cerebrovascular accident
P. W. Duncan, M. Propst, and S. G. Nelson · 1983
Earlier work this paper cites.
Learning internal representations by error propagation
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1990
Earlier work this paper cites.
Active perception and reinforcement learning
S. D. Whitehead and D. H. Ballard · 1990
Earlier work this paper cites.
How baseball outfielders determine where to run to catch fly balls
M. K. McBeath, D. M. Shaffer, and M. K. Kaiser · 1995
Earlier work this paper cites.
Active learning with statistical models
D. A. Cohn, Z. Ghahramani, and M. I. Jordan · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
A sensorimotor account of vision and visual consciousness
J. K. O’regan and A. Noë · 2001
Earlier work this paper cites.
The role of top-down and bottom-up processes in guiding eye movements during visual search
G. Zelinsky, W. Zhang, B. Yu, X. Chen, and D. Samaras · 2005
Earlier work this paper cites.
Active reinforcement learning
A. Epshteyn, A. Vogel, and G. DeJong · 2008
Earlier work this paper cites.
A theory of active object localization
A. Andreopoulos and J. K. Tsotsos · 2009
Earlier work this paper cites.
Multi-class active learning for image classification
A. J. Joshi, F. Porikli, and N. Papanikolopoulos · 2009
Earlier work this paper cites.
Active learning for reward estimation in inverse reinforcement learning
M. Lopes, F. Melo, and L. Montesano · 2009
Earlier work this paper cites.
Principles of sensorimotor learning
D. M. Wolpert, J. Diedrichsen, and J. R. Flanagan · 2011
Earlier work this paper cites.
April: Active preference learning-based reinforcement learning
R. Akrour, M. Schoenauer, and M. Sebag · 2012
Earlier work this paper cites.
State-of-the-art in visual attention modeling
A. Borji and L. Itti · 2012
Earlier work this paper cites.
Value normalization in decision making: theory and evidence
A. Rangel and J. A. Clithero · 2012
Earlier work this paper cites.
A computational learning theory of active object recognition under uncertainty
A. Andreopoulos and J. K. Tsotsos · 2013
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Earlier work this paper cites.
Latent structured active learning
W. Luo, A. Schwing, and R. Urtasun · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning, 2013
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
Earlier work this paper cites.
Nonmyopic view planning for active object classification and pose estimation
N. Atanasov, B. Sankaran, J. Le Ny, G. J. Pappas, and K. Daniilidis · 2014
Earlier work this paper cites.
Active reward learning
C. Daniel, M. Viering, J. Metz, O. Kroemer, and J. Peters · 2014
Earlier work this paper cites.
Deep recurrent q-learning for partially observable mdps
M. J. Hausknecht and P. Stone · 2015
Earlier work this paper cites.
Robot-centric activity prediction from first-person videos: What will they do to me?
M. S. Ryoo, T. J. Fuchs, L. Xia, J. K. Aggarwal, and L. Matthies · 2015
Earlier work this paper cites.
Deep attention recurrent q-network, 2015
I. Sorokin, A. Seleznev, M. Pavlov, A. Fedorov, and A. Ignateva · 2015
Earlier work this paper cites.
Look-ahead before you leap: end-to-end active recognition by forecasting the effect of motion
D. Jayaraman and K. Grauman · 2016
Earlier work this paper cites.
Scene construction, visual foraging, and active inference
M. B. Mirza, R. A. Adams, C. D. Mathys, and K. J. Friston · 2016
Earlier work this paper cites.
Stream-based active learning for efficient and adaptive classification of 3d objects
A. Narr, R. Triebel, and D. Cremers · 2016
Earlier work this paper cites.
Learning values across many orders of magnitude
H. P. van Hasselt, A. Guez, M. Hessel, V. Mnih, and D. Silver · 2016
Earlier work this paper cites.
Deep bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2017
Earlier work this paper cites.
Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2017
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al · 2017
Earlier work this paper cites.
Third-person imitation learning
B. C. Stadie, P. Abbeel, and I. Sutskever · 2017
Earlier work this paper cites.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller · 2017
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Generative adversarial active learning
J.-J. Zhu and J. Bento · 2017
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Revisiting active perception
R. Bajcsy, Y. Aloimonos, and J. K. Tsotsos · 2018
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The power of ensembles for active learning in image classification
W. H. Beluch, T. Genewein, A. Nürnberger, and J. M. Köhler · 2018
Cited alongside, same era.
Learning actionable representations from visual observations
D. Dwibedi, J. Tompson, C. Lynch, and P. Sermanet · 2018
Cited alongside, same era.
Neuroevolution of self-interpretable agents
Y. Tang, D. Nguyen, and D. Ha · 2020
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Vision-and-dialog navigation
J. Thomason, M. Murray, M. Cakmak, and L. Zettlemoyer · 2020
Later among the works it cites.
dm_control: Software and tasks for continuous control
S. Tunyasuvunakool, A. Muldal, Y. Doron, S. Liu, S. Bohez, J. Merel, T. Erez, T. Lillicrap, N. Heess, and Y. Tassa · 2020
Later among the works it cites.
robosuite: A modular simulation framework and benchmark for robot learning
Y. Zhu, J. Wong, A. Mandlekar, R. Martín-Martín, A. Joshi, S. Nasiriany, and Y. Zhu · 2020
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
Later among the works it cites.
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
Cited alongside, same era.
Perceiving, learning, and recognizing 3d objects: An approach to cognitive service robots
S. Kasaei, J. Sock, L. S. Lopes, A. M. Tomé, and T.-K. Kim · 2018
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
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Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
D. Mahapatra, B. Bozorgtabar, J.-P. Thiran, and M. Reyes · 2018
Cited alongside, same era.
Gaze and the control of foot placement when walking in natural terrain
J. S. Matthis, J. L. Yates, and M. M. Hayhoe · 2018
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Time-contrastive networks: Self-supervised learning from video
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain · 2018
Cited alongside, same era.
J. Choi, K. M. Yi, J. Kim, J. Choo, B. Kim, J. Chang, Y. Gwon, and H. J. Chang · 2021
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Flar: A unified prototype framework for few-sample lifelong active recognition
L. Fan, P. Xiong, W. Wei, and Y. Wu · 2021
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Machine versus human attention in deep reinforcement learning tasks
S. S. Guo, R. Zhang, B. Liu, Y. Zhu, D. Ballard, M. Hayhoe, and P. Stone · 2021
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Offline reinforcement learning as one big sequence modeling problem
M. Janner, Q. Li, and S. Levine · 2021
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Message passing adaptive resonance theory for online active semi-supervised learning
T. Kim, I. Hwang, H. Lee, H. Kim, W.-S. Choi, J. J. Lim, and B.-T. Zhang · 2021
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Fast active learning for pure exploration in reinforcement learning
P. Ménard, O. D. Domingues, A. Jonsson, E. Kaufmann, E. Leurent, and M. Valko · 2021
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Embodied visual active learning for semantic segmentation
D. Nilsson, A. Pirinen, E. Gärtner, and C. Sminchisescu · 2021
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D-nerf: Neural radiance fields for dynamic scenes
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer · 2021
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Attention-privileged reinforcement learning
S. Salter, D. Rao, M. Wulfmeier, R. Hadsell, and I. Posner · 2021
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Self-supervised disentangled representation learning for third-person imitation learning
J. Shang and M. S. Ryoo · 2021
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Nearest neighbor classifier embedded network for active learning
F. Wan, T. Yuan, M. Fu, X. Ji, Q. Huang, and Q. Ye · 2021
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Self-supervised attention-aware reinforcement learning
H. Wu, K. Khetarpal, and D. Precup · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
D. Yarats, R. Fergus, A. Lazaric, and L. Pinto · 2021
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Multiple instance active learning for object detection
T. Yuan, F. Wan, M. Fu, J. Liu, S. Xu, X. Ji, and Q. Ye · 2021
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Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning
T. Zhang, Y. Li, C. Wang, G. Xie, and Z. Lu · 2021
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Stabilizing off-policy deep reinforcement learning from pixels
E. Cetin, P. J. Ball, S. Roberts, and O. Celiktutan · 2022
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Alds: An active learning method for multi-source materials data screening and materials design
S. Chen, H. Cao, Q. Ouyang, X. Wu, and Q. Qian · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
L. Fan, G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar · 2022
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Deep reinforcement learning with active vision on atari environments
R. Göransson · 2022
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Vision-based manipulators need to also see from their hands
K. Hsu, M. J. Kim, R. Rafailov, J. Wu, and C. Finn · 2022
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Q-attention: Enabling efficient learning for vision-based robotic manipulation
S. James and A. J. Davison · 2022
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Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
S. James, K. Wada, T. Laidlow, and A. J. Davison · 2022
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Towards robust and reproducible active learning using neural networks
P. Munjal, N. Hayat, M. Hayat, J. Sourati, and S. Khan · 2022
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M. S. Ryoo, K. Gopalakrishnan, K. Kahatapitiya, T. Xiao, K. Rao, A. Stone, Y. Lu, J. Ibarz, and A. Arnab · 2022
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Embodied object representation learning and recognition
T. Van de Maele, T. Verbelen, O. Çatal, and B. Dhoedt · 2022
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Avoiding lingering in learning active recognition by adversarial disturbance
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