Aleksi Hämäläinen

Affordance Learning for End-to-End Visuomotor Robot Control

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 issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and traject…