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cs.CV2019
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh +1
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this wo…
cs.RO2019
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…