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We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone.
Long short-term memory
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Natural language command of an autonomous micro-air vehicle
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Learning to parse natural language commands to a robot control system
C. Matuszek, E. Herbst, L. Zettlemoyer, and D. Fox · 2012
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A Joint Model of Language and Perception for Grounded Attribute Learning
C. Matuszek, N. FitzGerald, L. Zettlemoyer, L. Bo, and D. Fox · 2012
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Robot learning from demonstration by constructing skill trees
G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto · 2012
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Imitation learning for natural language direction following through unknown environments
F. Duvallet, T. Kollar, and A. Stentz · 2013
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Learning Semantic Maps from Natural Language Descriptions
M. R. Walter, S. Hemachandra, B. Homberg, S. Tellex, and S. Teller · 2013
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Y. Artzi and L. Zettlemoyer · 2013
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Probabilistic movement primitives
A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann · 2013
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Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
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Tell me dave: Context-sensitive grounding of natural language to mobile manipulation instructions
D. K. Misra, J. Sung, K. Lee, and A. Saxena · 2014
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Learning to interpret natural language commands through human-robot dialog
J. Thomason, S. Zhang, R. J. Mooney, and P. Stone · 2015
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Learning models for following natural language directions in unknown environments
S. Hemachandra, F. Duvallet, T. M. Howard, N. Roy, A. Stentz, and M. R. Walter · 2015
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Recovering from Failure by Asking for Help
R. A. Knepper, S. Tellex, A. Li, N. Roy, and D. Rus · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cognitive mapping and planning for visual navigation
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Active incremental learning of robot movement primitives
G. Maeda, M. Ewerton, T. Osa, B. Busch, and J. Peters · 2017
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
S. Shah, D. Dey, C. Lovett, and A. Kapoor · 2017
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Sequence-to-sequence language grounding of non-markovian task specifications
N. Gopalan, D. Arumugam, L. L. Wong, and S. Tellex · 2018
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Following high-level navigation instructions on a simulated quadcopter with imitation learning
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Learning hand-eye coordination for robotic grasping with large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen · 2016
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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Playing doom with slam-augmented deep reinforcement learning
S. Bhatti, A. Desmaison, O. Miksik, N. Nardelli, N. Siddharth, and P. H. Torr · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Accurately and efficiently interpreting human-robot instructions of varying granularities
D. Arumugam, S. Karamcheti, N. Gopalan, L. L. Wong, and S. Tellex · 2017
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Mapping instructions and visual observations to actions with reinforcement learning
D. Misra, J. Langford, and Y. Artzi · 2017
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V. Blukis, N. Brukhim, A. Bennet, R. Knepper, and Y. Artzi · 2018
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Mapping instructions to actions in 3D environments with visual goal prediction
D. Misra, A. Bennett, V. Blukis, E. Niklasson, M. Shatkin, and Y. Artzi · 2018
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Gated-attention architectures for task-oriented language grounding
D. S. Chaplot, K. M. Sathyendra, R. K. Pasumarthi, D. Rajagopal, and R. Salakhutdinov · 2018
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Situated mapping of sequential instructions to actions with single-step reward observation
A. Suhr and Y. Artzi · 2018
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P. Shah, M. Fiser, A. Faust, J. C. Kew, and D. Hakkani-Tur · 2018
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Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods
D. Quillen, E. Jang, O. Nachum, C. Finn, J. Ibarz, and S. Levine · 2018
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Universal planning networks
A. Srinivas, A. Jabri, P. Abbeel, S. Levine, and C. Finn · 2018
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, et al · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke · 2018
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Memory augmented control networks
A. Khan, C. Zhang, N. Atanasov, K. Karydis, V. Kumar, and D. D. Lee · 2018
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Semi-parametric topological memory for navigation
N. Savinov, A. Dosovitskiy, and V. Koltun · 2018
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