Search Results for author: Michael Gienger

Found 5 papers, 0 papers with code

Distilled Domain Randomization

no code implementations6 Dec 2021 Julien Brosseit, Benedikt Hahner, Fabio Muratore, Michael Gienger, Jan Peters

However, these methods are notorious for the enormous amount of required training data which is prohibitively expensive to collect on real robots.

reinforcement-learning

Robot Learning from Randomized Simulations: A Review

no code implementations1 Nov 2021 Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.

Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty

no code implementations18 Oct 2021 Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah

Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models.

Data-efficient Domain Randomization with Bayesian Optimization

no code implementations5 Mar 2020 Fabio Muratore, Christian Eilers, Michael Gienger, Jan Peters

Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) during training according to a distribution over domain parameters in order to obtain more robust policies that are able to overcome the reality gap.

Assessing Transferability from Simulation to Reality for Reinforcement Learning

no code implementations10 Jul 2019 Fabio Muratore, Michael Gienger, Jan Peters

Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the `Simulation Optimization Bias` (SOB).

reinforcement-learning

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