2019
GAPLE: Generalizable Approaching Policy LEarning for Robotic Object Searching in Indoor Environment
RA-L 2019
We study the problem of learning a generalizable action policy for an intelligent agent to actively approach an object of interest, in an indoor environment, solely from its visual inputs. While scene-driven or recognition-driven visual navigation has been widely studied, prior efforts suffer severe