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Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments.
Divergence measures and message passing
T. Minka · 2005
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Pattern recognition and machine learning
C. M. Bishop · 2006
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Automatic Gait Optimization with Gaussian Process Regression
D. J. Lizotte, T. Wang, M. H. Bowling, and D. Schuurmans · 2007
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Online optimization of swimming and crawling in an amphibious snake robot
A. Crespi and A. J. Ijspeert · 2008
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Combining active learning and reactive control for robot grasping
O. Kroemer, R. Detry, J. Piater, and J. Peters · 2010
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Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. E. Rasmussen · 2011
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Using response surfaces and expected improvement to optimize snake robot gait parameters
M. Tesch, J. Schneider, and H. Choset · 2011
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Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
L. Montesano and M. Lopes · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning
A. Wilson, A. Fern, and P. Tadepalli · 2014
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Semi-supervised learning with deep generative models
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling · 2014
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Optimization-based Full Body Control for the DARPA Robotics Challenge
S. Feng, E. Whitman, X. Xinjilefu, and C. G. Atkeson · 2015
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Robots that can adapt like animals
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret · 2015
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Manifold gaussian processes for regression
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth · 2016
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Deep kernel learning
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing · 2016
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The variational fair autoencoder
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel · 2016
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Taking the Human Out of the Loop: A Review of Bayesian Optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2016
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A disentangled recognition and nonlinear dynamics model for unsupervised learning
M. Fraccaro, S. Kamronn, U. Paquet, and O. Winther · 2017
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Bayesian Modeling for Optimization and Control in Robotics
R. Calandra · 2017
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Quasi-recurrent neural networks
J. Bradbury, S. Merity, C. Xiong, and R. Socher · 2017
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Zero-shot skill composition and simulation-to-real transfer by learning task representations
Z. He, R. Julian, E. Heiden, H. Zhang, S. Schaal, J. Lim, G. Sukhatme, and K. Hausman · 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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Using deep reinforcement learning to learn high-level policies on the atrias biped
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OpenAI · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
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State representation learning for control: An overview
T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, and D. Filliat · 2018
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Disentangled sequential autoencoder
L. Yingzhen and S. Mandt · 2018
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A method for online optimization of lower limb assistive devices with high dimensional parameter spaces
N. Thatte, H. Duan, and H. Geyer · 2018
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Feedback control of a cassie bipedal robot: Walking, standing, and riding a segway
Y. Gong, R. Hartley, X. Da, A. Hereid, O. Harib, J.-K. Huang, and J. Grizzle · 2018
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T. Li, A. Rai, H. Geyer, and C. G. Atkeson · 2018
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Failure modes of variational inference for decision making
C. Riquelme, M. Johnson, and M. Hoffman · 2018
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Variational inference for data-efficient model learning in pomdps
S. Tschiatschek, K. Arulkumaran, J. Stühmer, and K. Hofmann · 2018
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
S. Bai, J. Z. Kolter, and V. Koltun · 2018
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Using simulation to improve sample-efficiency of bayesian optimization for bipedal robots
A. Rai, R. Antonova, F. Meier, and C. G. Atkeson · 2019
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VPE: Variational Policy Embedding for Transfer Reinforcement Learning
I. Arnekvist, D. Kragic, and J. A. Stork · 2019
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Learning to walk via deep reinforcement learning
T. Haarnoja, S. Ha, A. Zhou, J. Tan, G. Tucker, and S. Levine · 2019
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http://docs.hebi.us
Hebi Robotics · 2019
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https://github.com/bulletphysics/bullet3
Pybullet simulator · 2019
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