Autonomous Development of a Grounded Object Ontology by a Learning Robot

We describe how a physical robot can learn about objects from its own autonomous experience in the continuous world. The robot identifies statistical regularities that allow it to represent a physical object with a cluster of sensations that violate a static world model, track that cluster over time, extract percepts from that cluster, form concepts from similar percepts, and learn reliable actions that can be applied to objects. We present a formalism for representing the ontology for objects and actions, a learning algorithm, and the results of an evaluation with a physical robot.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Joseph Modayil, Benjamin Kuipers
• Keywords: AAAI 2007, Extract Percepts, Intelligent Agents, Physical Robot, Static World Model