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Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to real-world deployment.
Parameter identification of robot dynamics
P. K. Khosla and T. Kanade · 1985
Earlier work this paper cites.
On the identification of the inertial parameters of robots
M. Gautier and W. Khalil · 1988
Earlier work this paper cites.
Stochastic control of partially observable systems
A. Bensoussan · 1992
Earlier work this paper cites.
Optimal robot excitation and identification
J. Swevers, C. Ganseman, D. B. Tukel, J. De Schutter, and H. Van Brussel · 1997
Earlier work this paper cites.
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Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam, et al · 2022
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Velocity Level Approximation of Pressure Field Contact Patches
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