Amortized Object and Scene Perception for Long-term Robot Manipulation
This addresses the challenge of reliable object tracking in dynamic human environments for robotics, though it appears incremental in its approach.
The paper tackles the problem of enabling mobile robots to perceive and track diverse objects over long-term manipulation tasks by introducing an amortized perception system that asynchronously integrates logged images into a perceptual belief state.
Mobile robots, performing long-term manipulation activities in human environments, have to perceive a wide variety of objects possessing very different visual characteristics and need to reliably keep track of these throughout the execution of a task. In order to be efficient, robot perception capabilities need to go beyond what is currently perceivable and should be able to answer queries about both current and past scenes. In this paper we investigate a perception system for long-term robot manipulation that keeps track of the changing environment and builds a representation of the perceived world. Specifically we introduce an amortized component that spreads perception tasks throughout the execution cycle. The resulting query driven perception system asynchronously integrates results from logged images into a symbolic and numeric (what we call sub-symbolic) representation that forms the perceptual belief state of the robot.