Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots

Developing an autonomous mobile robot which can reliably search, locate and reach an arbitrary object in an indoor environment is both fascinating and extremely challenging which motivates multi-disciplinary research ideas across robotics, computational perception, machine learning. In practice, a solution to this task will have a wide range of robotics applications, such as an assistant robot to search for survivors from an unknown disastrous environment for the first responders, or an elderly care-giving robot to locate and/or retrieve objects of interest for its clients. Solving this challenge has the potential to kick off the next phase of our human life style revolution that aims to increase people's living standard and enrich people's everyday life. We fully acknowledge that studies approaching the problem have a long history. Tracing back to the 1970s and 1980s, when the concept coined as the "active perception" was widely explored, this "robot with vision that finds object" task was one of the major motivating tasks to show that "vision is active" [1]. As stated in a recent survey article [2], two primary aspects of "active perception" are 1) from intelligent control point of view, it is about intelligent control strategies applied to the perception process [3], and 2) from computational perception point of view, it is about manipulating the perception constraints to improve the quality of