1 citations · 3 across the 4 of their papers we have counts for
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SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
Rituraj Kaushik, Karol Arndt, Ville Kyrki
The framework of Simulation-to-real learning, i.e, learning policies in simulation and transferring those policies to the real world is one of the most promising approaches towards…
Affine Transport for Sim-to-Real Domain Adaptation
Anton Mallasto, Karol Arndt, Markus Heinonen +2
Sample-efficient domain adaptation is an open problem in robotics. In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between st…
Affordance Learning for End-to-End Visuomotor Robot Control
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh +1
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issu…