Bootstrap learning of foundational representations

Benjamin J. Kuipers, Patrick Beeson, Joseph Modayil, Jefferson Provost
2006 Connection science  
To be autonomous, intelligent robots must learn the foundations of commonsense knowledge from their own sensorimotor experience in the world. We describe four recent research results that contribute to a theory of how a robot learning agent can bootstrap from the 'blooming buzzing confusion' of the pixel level to a higher-level ontology including distinctive states, places, objects, and actions. This is not a single learning problem, but a lattice of related learning tasks, each providing
more » ... uisites for tasks to come later. Starting with completely uninterpreted sense and motor vectors, as well as an unknown environment, we show how a learning agent can separate the sense vector into modalities, learn the structure of individual modalities, learn natural primitives for the motor system, identify reliable relations between primitive actions and created sensory features, and can define useful control laws for homing and pathfollowing. Building on this framework, we show how an agent can use self-organizing maps to identify useful sensory featurs in the environment, and can learn effective hill-climbing control laws to define distinctive states in terms of those features, and trajectory-following control laws to move from one distinctive state to another. Moving on to place recognition, we show how an agent can combine unsupervised learning, map-learning, and supervised learning to achieve high-performance recognition of places from rich sensory input. And finally, we take the first steps toward learning an ontology of objects, showing that a bootstrap learning robot can learn to individuate objects through motion, separating them from the static environment and from each other, and can learn properties useful for classification. These are four key steps in a larger research enterprise on the foundations of human and robot commonsense knowledge.
doi:10.1080/09540090600768484 fatcat:ckqjttktnvdbtb4ekftx3q22gq