Task-adaptable, Pervasive Perception for Robots Performing Everyday Manipulation

Abstract

Intelligent robotic agents that help us in our day-to-day chores have been an aspiration of robotics researchers for decades. More than fifty years since the creation of the first intelligent mobile robotic agent, robots are still struggling to perform seemingly simple tasks, such as setting or cleaning a table. One of the reasons for this is that the unstructured environments these robots are expected to work in impose demanding requirements on a robota s perception system. Depending on the manipulation task the robot is required to execute, different parts of the environment need to be examined, the objects in it found and functional parts of these identified. This is a challenging task, since the visual appearance of the objects and the variety of scenes they are found in are large. This thesis proposes to treat robotic visual perception for everyday manipulation tasks as an open question-asnswering problem. To this end RoboSherlock, a framework for creating task-adaptable, pervasive perception systems is presented. Using the framework, robot perception is addressed from a systema s perspective and contributions to the state-of-the-art are proposed that introduce several enhancements which scale robot perception toward the needs of human-level manipulation. The contributions of the thesis center around task-adaptability and pervasiveness of perception systems. A perception task-language and a language interpreter that generates task-relevant perception plans is proposed. The task-language and task-interpreter leverage the power of knowledge representation and knowledge-based reasoning in order to enhance the question-answering capabilities of the system. Pervasiveness, a seamless integration of past, present and future percepts, is achieved through three main contributions: a novel way for recording, replaying and inspecting perceptual episodic memories, a new perception component that enables pervasive operation and maintains an object belief state and a novel prospection component that enables robots to relive their past experiences and anticipate possible future scenarios. The contributions are validated through several real world robotic experiments that demonstrate how the proposed system enhances robot perception.