1 code implementation • Neural Computing and Applications 2022 • Ran Emuna, Rotem Duffney, Avinoam Borowsky, Armin Biess
Here we introduce a model-free and easy-to-implement deep reinforcement learning approach to mimic the stochastic behavior of a human expert by learning distributions of task variables from examples.
no code implementations • 21 Aug 2020 • Binyamin Manela, Armin Biess
Learning complex tasks from scratch is challenging and often impossible for humans as well as for artificial agents.
1 code implementation • 7 Jun 2020 • Ran Emuna, Avinoam Borowsky, Armin Biess
However, human-controlled vehicles are likely to remain on the roads for several decades to come and may share with AVs the traffic environments of the future.
no code implementations • 25 Feb 2020 • Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential.
no code implementations • 28 May 2019 • Hanan Zaichyk, Armin Biess, Aryeh Kontorovich, Yury Makarychev
We introduce a framework for performing regression between two Hilbert spaces.
1 code implementation • 14 May 2019 • Binyamin Manela, Armin Biess
We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals.
no code implementations • 27 Sep 2018 • Yuval Litvak, Armin Biess, Aharon Bar-Hillel
We obtain an average pose estimation error of 2. 16 millimeters and 0. 64 degree leading to 91% success rate for robotic assembly of randomly distributed parts.