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We describe a framework for research and evaluation in Embodied AI.
Computer simulation of human thinking and problem solving
H. A. Simon and A. Newell · 1962
Earlier work this paper cites.
STRIPS: A new approach to the application of theorem proving to problem solving
R. E. Fikes and N. J. Nilsson · 1971
Earlier work this paper cites.
A mathematical introduction to robotic manipulation
R. M. Murray, Z. Li, and S. S. Sastry · 1994
Earlier work this paper cites.
Artificial Intelligence : A Modern Approach
S. Russell and P. Norvig · 1995
Earlier work this paper cites.
The RoboCup synthetic agent challenge 97
H. Kitano, M. Tambe, P. Stone, M. Veloso, S. Coradeschi, E. Osawa, H. Matsubara, I. Noda, and M. Asada · 1997
Earlier work this paper cites.
Practical pushing planning for rearrangement tasks
O. Ben-Shahar and E. Rivlin · 1998
Earlier work this paper cites.
PDDL - the planning domain definition language
M. Ghallab, C. Knoblock, D. Wilkins, A. Barrett, D. Christianson, M. Friedman, C. Kwok, K. Golden, S. Penberthy, D. Smith, Y. Sun, and D. Weld · 1998
Earlier work this paper cites.
The 1998 AI planning systems competition
D. M. McDermott · 2000
Earlier work this paper cites.
A pick-me-up for infants’ exploratory skills: Early simulated experiences reaching for objects using ‘sticky mittens’ enhances young infants’ object exploration skills
A. Needham, T. Barrett, and K. Peterman · 2002
Earlier work this paper cites.
Reinforcement learning for RoboCup soccer keepaway
P. Stone, R. S. Sutton, and G. Kuhlmann · 2005
Earlier work this paper cites.
Planning Algorithms
S. M. LaValle · 2006
Earlier work this paper cites.
Object tracking: A survey
A. Yilmaz, O. Javed, and M. Shah · 2006
Earlier work this paper cites.
Manipulation planning among movable obstacles
M. Stilman, J.-U. Schamburek, J. Kuffner, and T. Asfour · 2007
Earlier work this paper cites.
Visual navigation for mobile robots: A survey
F. Bonin-Font, A. Ortiz, and G. Oliver · 2008
Earlier work this paper cites.
Retrographic sensing for the measurement of surface texture and shape
M. K. Johnson and E. H. Adelson · 2009
Earlier work this paper cites.
Universal robotic gripper based on the jamming of granular material
E. Brown, N. Rodenberg, J. Amend, A. Mozeika, E. Steltz, M. R. Zakin, H. Lipson, and H. M. Jaeger · 2010
Earlier work this paper cites.
Benchmarking grasping and manipulation: Properties of the objects of daily living
K. Matheus and A. M. Dollar · 2010
Earlier work this paper cites.
Combining motion planning and optimization for flexible robot manipulation
J. Scholz and M. Stilman · 2010
Earlier work this paper cites.
Push planning for object placement on cluttered table surfaces
A. Cosgun, T. Hermans, V. Emeli, and M. Stilman · 2011
Earlier work this paper cites.
Hierarchical task and motion planning in the now
L. Kaelbling and T. Lozano-Perez · 2011
Earlier work this paper cites.
Generating text with recurrent neural networks
I. Sutskever, J. Martens, and G. E. Hinton · 2011
Earlier work this paper cites.
The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation
S. Ulbrich, D. Kappler, T. Asfour, N. Vahrenkamp, A. Bierbaum, M. Przybylski, and R. Dillmann · 2011
Earlier work this paper cites.
RoboCup@Home: Demonstrating everyday manipulation skills in RoboCup@Home
J. Stuckler, D. Holz, and S. Behnke · 2012
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.
A Concise Introduction to Models and Methods for Automated Planning
H. Geffner and B. Bonet · 2013
Earlier work this paper cites.
Object search by manipulation
M. R. Dogar, M. C. Koval, A. Tallavajhula, and S. S. Srinivasa · 2014
Earlier work this paper cites.
Rearranging similar objects with a manipulator using pebble graphs
A. Krontiris, R. Shome, A. Dobson, A. Kimmel, and K. Bekris · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
Cited alongside, same era.
The YCB object and model set: Towards common benchmarks for manipulation research
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
Cited alongside, same era.
The Pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. J. V. Gool, C. K. I. Williams, J. M. Winn, and A. Zisserman · 2015
Cited alongside, same era.
Visual simultaneous localization and mapping: A survey
J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha · 2015
Cited alongside, same era.
Affordance detection of tool parts from geometric features
A. Myers, C. L. Teo, C. Fermüller, and Y. Aloimonos · 2015
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
C. J. Cueva and X.-X. Wei · 2018
Later among the works it cites.
FutureMapping: The computational structure of Spatial AI systems
A. J. Davison · 2018
Later among the works it cites.
Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
L. Fan, Y. Zhu, J. Zhu, Z. Liu, O. Zeng, A. Gupta, J. Creus-Costa, S. Savarese, and L. Fei-Fei · 2018
Later among the works it cites.
Guest editorial open discussion of robot grasping benchmarks, protocols, and metrics
J. Mahler, R. Platt, A. Rodriguez, M. Ciocarlie, A. Dollar, R. Detry, M. A. Roa, H. Yanco, A. Norton, J. Falco, et al · 2018
Later among the works it cites.
Toward robotic manipulation
M. T. Mason · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. B. Girshick, and J. Sun · 2015
Cited alongside, same era.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2015
Cited alongside, same era.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Cited alongside, same era.
Automated Planning and Acting
M. Ghallab, D. Nau, and P. Traverso · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Team Delft’s robot winner of the Amazon picking challenge 2016
C. Hernandez, M. Bharatheesha, W. Ko, H. Gaiser, J. Tan, K. van Deurzen, M. de Vries, B. Van Mil, J. van Egmond, R. Burger, et al · 2016
Cited alongside, same era.
Gibson env: Real-world perception for embodied agents
F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese · 2018
Later among the works it cites.
Mechanical search: Multi-step retrieval of a target object occluded by clutter
M. Danielczuk, A. Kurenkov, A. Balakrishna, M. Matl, D. Wang, R. Martín-Martín, A. Garg, S. Savarese, and K. Goldberg · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
Later among the works it cites.
Habitat: A platform for embodied AI research
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra · 2019
Later among the works it cites.
XLNet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le · 2019
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2019
Later among the works it cites.
Does computer vision matter for action?
B. Zhou, P. Krähenbühl, and V. Koltun · 2019
Later among the works it cites.
ObjectNav revisited: On evaluation of embodied agents navigating to objects
D. Batra, A. Gokaslan, A. Kembhavi, O. Maksymets, R. Mottaghi, M. Savva, A. Toshev, and E. Wijmans · 2020
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Y. Bisk, A. Holtzman, J. Thomason, J. Andreas, Y. Bengio, J. Chai, M. Lapata, A. Lazaridou, J. May, A. Nisnevich, N. Pinto, and J. Turian · 2020
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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D4RL: Datasets for deep data-driven reinforcement learning
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine · 2020
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ThreeDWorld: A platform for interactive multi-modal physical simulation
C. Gan, J. Schwartz, S. Alter, M. Schrimpf, J. Traer, J. De Freitas, J. Kubilius, A. Bhandwaldar, N. Haber, M. Sano, et al · 2020
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2020
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RLBench: The robot learning benchmark & learning environment
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison · 2020
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Automated planning for robotics
E. Karpas and D. Magazzeni · 2020
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Deep learning for generic object detection: A survey
L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, and M. Pietikäinen · 2020
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Synchronized multi-arm rearrangement guided by mode graphs with capacity constraints
R. Shome and K. E. Bekris · 2020
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MoreFusion: Multi-object reasoning for 6D pose estimation from volumetric fusion
K. Wada, E. Sucar, S. James, D. Lenton, and A. J. Davison · 2020
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SAPIEN: A simulated part-based interactive environment
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su · 2020
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Integrated task and motion planning
C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez · 2021
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