  "Looking for things" is a mundane but critical task we repeatedly carry on in
our daily life. We introduce a method to develop a human character capable of
searching for a randomly located target object in a detailed 3D scene using its
locomotion capability and egocentric vision perception represented as RGBD
images. By depriving the privileged 3D information from the human character, it
is forced to move and look around simultaneously to account for the restricted
sensing capability, resulting in natural navigation and search behaviors. Our
method consists of two components: 1) a search control policy based on an
abstract character model, and 2) an online replanning control module for
synthesizing detailed kinematic motion based on the trajectories planned by the
search policy. We demonstrate that the combined techniques enable the character
to effectively find often occluded household items in indoor environments. The
same search policy can be applied to different full-body characters without the
need for retraining. We evaluate our method quantitatively by testing it on
randomly generated scenarios. Our work is a first step toward creating
intelligent virtual agents with humanlike behaviors driven by onboard sensors,
paving the road toward future robotic applications.
