  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 severely from the limited generalization capability. In this paper, we
first argue the object searching task is environment dependent while the
approaching ability is general. To learn a generalizable approaching policy, we
present a novel solution dubbed as GAPLE which adopts two channels of visual
features: depth and semantic segmentation, as the inputs to the policy learning
module. The empirical studies conducted on the House3D dataset as well as on a
physical platform in a real world scenario validate our hypothesis, and we
further provide in-depth qualitative analysis.
