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Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction.
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
Halbert White · 1980
Earlier work this paper cites.
Survey of numerical methods for trajectory optimization
John T Betts · 1998
Earlier work this paper cites.
Conditions for interference versus facilitation during sequential sensorimotor adaptation
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Synthesis and stabilization of complex behaviors through online trajectory optimization
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Earlier work this paper cites.
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Peter W. Battaglia, Jessica B. Hamrick, and Joshua B. Tenenbaum · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Trust region policy optimization
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Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Online semi-parametric learning for inverse dynamics modeling
D. Romeres, M. Zorzi, R. Camoriano, and A. Chiuso · 2016
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Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Data-efficient control policy search using residual dynamics learning
Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing
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Sim-to-real transfer learning using robustified controllers in robotic tasks involving complex dynamics
J. v. Baar, A. Sullivan, R. Corcodel, D. Jha, D. Romeres, and D. Nikovski · 2019
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Semiparametrical gaussian processes learning of forward dynamical models for navigating in a circular maze
D. Romeres, D. K. Jha, A. DallaLibera, B. Yerazunis, and D. Nikovski · 2019
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Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Joshua B Tenenbaum, Alberto Rodriguez, and Leslie P Kaelbling · 2019
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Drake: Model-based design and verification for robotics, 2019
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
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Model-based reinforcement learning for physical systems without velocity and acceleration measurements
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Mujoco · 2020
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