Authors:
Andreas Hofmann
1
and
Paul Robertson
2
Affiliations:
1
Dynamic Object Language Labs, Inc. and Massachusetts Institute of Technology, United States
;
2
Dynamic Object Language Labs and Inc., United States
Keyword(s):
Active Perception, POMDP, Belief State Planning.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Mobile Imaging
;
Motion, Tracking and Stereo Vision
;
Pervasive Smart Cameras
Abstract:
Existing machine perception systems are too inflexible, and therefore cannot adapt well to environment uncertainty. We address this problem through a more dynamic approach in which reasoning about context is used to actively and effectively allocate and focus sensing and action resources. This Active Perception approach prioritizes the system’s overall goals, so that perception and situation awareness are well integrated with actions to focus all efforts on these goals in an optimal manner. We use a POMDP (Partially Observable Markov Decision Process) framework, but do not attempt to compute a comprehensive control policy, as this is intractible for practical problems. Instead, we employ Belief State Planning to compute point solutions from an initial state to a goal state set. This approach automatically generates action sequences for sensing operations that reduce uncertainty in the belief state, and ultimately achieve the goal state set.