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Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen).
The ecological approach to visual perception: classic edition
J. J. Gibson · 1979
Earlier work this paper cites.
Curious model-building control systems
J. Schmidhuber · 1991
Earlier work this paper cites.
An analysis of model-based interval estimation for markov decision processes
A. L. Strehl and M. L. Littman · 2008
Earlier work this paper cites.
Observing human-object interactions: Using spatial and functional compatibility for recognition
A. Gupta, A. Kembhavi, and L. S. Davis · 2009
Earlier work this paper cites.
Scene semantics from long-term observation of people
V. Delaitre, D. F. Fouhey, I. Laptev, J. Sivic, A. Gupta, and A. A. Efros · 2012
Earlier work this paper cites.
You-do, i-learn: Discovering task relevant objects and their modes of interaction from multi-user egocentric video
D. Damen, T. Leelasawassuk, O. Haines, A. Calway, and W. W. Mayol-Cuevas · 2014
Earlier work this paper cites.
Physically grounded spatio-temporal object affordances
H. S. Koppula and A. Saxena · 2014
Earlier work this paper cites.
Scenegrok: Inferring action maps in 3d environments
M. Savva, A. X. Chang, P. Hanrahan, M. Fisher, and M. Nießner · 2014
Earlier work this paper cites.
Interactive affordance map building for a robotic task
D. I. Kim and G. S. Sukhatme · 2015
Earlier work this paper cites.
Variational information maximisation for intrinsically motivated reinforcement learning
S. Mohamed and D. J. Rezende · 2015
Earlier work this paper cites.
Learning to pick up objects through active exploration
J. Oberlin and S. Tellex · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Understanding tools: Task-oriented object modeling, learning and recognition
Y. Zhu, Y. Zhao, and S. Chun Zhu · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
M. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos · 2016
Earlier work this paper cites.
A deep multi-level network for saliency prediction
M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara · 2016
Earlier work this paper cites.
Anticipating human activities using object affordances for reactive robotic response
H. S. Koppula and A. Saxena · 2016
Earlier work this paper cites.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
Earlier work this paper cites.
Learning action maps of large environments via first-person vision
N. Rhinehart and K. M. Kitani · 2016
Earlier work this paper cites.
Cascaded interactional targeting network for egocentric video analysis
Y. Zhou, B. Ni, R. Hong, X. Yang, and Q. Tian · 2016
Earlier work this paper cites.
Inferring forces and learning human utilities from videos
Y. Zhu, C. Jiang, Y. Zhao, D. Terzopoulos, and S.-C. Zhu · 2016
Earlier work this paper cites.
Joint discovery of object states and manipulation actions
J.-B. Alayrac, J. Sivic, I. Laptev, and S. Lacoste-Julien · 2017
Earlier work this paper cites.
Home: A household multimodal environment
S. Brodeur, E. Perez, A. Anand, F. Golemo, L. Celotti, F. Strub, J. Rouat, H. Larochelle, and A. Courville · 2017
Cited alongside, same era.
Matterport3d: Learning from rgb-d data in indoor environments
A. Chang, A. Dai, T. Funkhouser, , M. Nießner, M. Savva, S. Song, A. Zeng, and Y. Zhang · 2017
Cited alongside, same era.
Learning to fly by crashing
D. Gandhi, L. Pinto, and A. Gupta · 2017
Cited alongside, same era.
AI2-THOR: An Interactive 3D Environment for Visual AI
E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi · 2017
Cited alongside, same era.
Ai2-thor: An interactive 3d environment for visual ai
E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi · 2017
Cited alongside, same era.
Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, et al · 2018
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Solving rubik’s cube with a robot hand
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, et al · 2019
Later among the works it cites.
Large-scale study of curiosity-driven learning
Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. A. Efros · 2019
Later among the works it cites.
Learning exploration policies for navigation
T. Chen, S. Gupta, and A. Gupta · 2019
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Scene memory transformer for embodied agents in long-horizon tasks
K. Fang, A. Toshev, L. Fei-Fei, and S. Savarese · 2019
Later among the works it cites.
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Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg · 2017
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Combining self-supervised learning and imitation for vision-based rope manipulation
A. Nair, D. Chen, P. Agrawal, P. Isola, P. Abbeel, J. Malik, and S. Levine · 2017
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Combining self-supervised learning and imitation for vision-based rope manipulation
A. Nair, D. Chen, P. Agrawal, P. Isola, P. Abbeel, J. Malik, and S. Levine · 2017
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Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Cited alongside, same era.
Binge watching: Scaling affordance learning from sitcoms
X. Wang, R. Girdhar, and A. Gupta · 2017
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Visual semantic planning using deep successor representations
Y. Zhu, D. Gordon, E. Kolve, D. Fox, L. Fei-Fei, A. Gupta, R. Mottaghi, and A. Farhadi · 2017
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Z. Gao, R. Gong, T. Shu, X. Xie, S. Wang, and S. C. Zhu · 2019
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L. K. L. Goff, G. Mukhtar, A. Coninx, and S. Doncieux · 2019
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Building an affordances map with interactive perception
L. K. L. Goff, O. Yaakoubi, A. Coninx, and S. Doncieux · 2019
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Learning latent plans from play
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet · 2019
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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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Grounded human-object interaction hotspots from video
T. Nagarajan, C. Feichtenhofer, and K. Grauman · 2019
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Episodic curiosity through reachability
N. Savinov, A. Raichuk, R. Marinier, D. Vincent, M. Pollefeys, T. Lillicrap, and S. Gelly · 2019
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The Replica dataset: A digital replica of indoor spaces
J. Straub, T. Whelan, L. Ma, Y. Chen, E. Wijmans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma, A. Clarkson, M. Yan, B. Budge, Y. Yan, X. Pan, J. Yon, Y. Zou, K. Leon, N. Carter, J. Briales, T. Gillingham, E. Mueggler, L. Pesqueira, M. Savva, D. Batra, H. M. Strasdat, R. D. Nardi, M. Goesele, S. Lovegrove, and R. Newcombe · 2019
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http://svl.stanford.edu/igibson/challenge.html , 2020
Gibson sim2real challenge · 2020
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https://ai2thor.allenai.org/robothor/challenge/ , 2020
Robothor · 2020
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Learning to explore using active neural slam
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Ego-topo: Environment affordances from egocentric video
T. Nagarajan, Y. Li, C. Feichtenhofer, and K. Grauman · 2020
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Learning to move with affordance maps
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An exploration of embodied visual exploration
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox · 2020
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Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments
F. Xia, W. B. Shen, C. Li, P. Kasimbeg, M. E. Tchapmi, A. Toshev, R. Martín-Martín, and S. Savarese · 2020
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