Scaling-up Knowledge for a Cognizant Robot

This paper takes a new approach to the old adage that knowledge is the key for artificial intelligence. A cognizant robot is a robot with a deep and immediately accessible understanding of its interaction with the environment — an understanding the robot can use to flexibly adapt to novel situations. Such a robot will need a vast amount of situated, revisable, and expressive knowledge to display flexible intelligent behaviors. Instead of relying on human-provided knowledge, we propose that an arbitrary robot can autonomously acquire pertinent knowledge directly from everyday interaction with the environment. We show how existing ideas in reinforcement learning can enable a robot to maintain and improve its knowledge. The robot performs a continual learning process that scales-up knowledge acquisition to cover a large number of facts, skills and predictions. This knowledge has semantics that are grounded in sensorimotor experience. We see the approach of developing more cognizant robots as a necessary key step towards broadly competent robots.