Learning Grounded Language through Situated Interactive Instruction

We present an approach for learning grounded language from mixed-initiative human-robot interaction. Prior work on learning from human instruction has concentrated on acquisition of task-execution knowledge from domainspecific language. In this work, we demonstrate acquisition of linguistic, semantic, perceptual, and procedural knowledge from mixed-initiative, natural language dialog. Our approach has been instantiated in a cognitive architecture, Soar, and has been deployed on a table-top robotic arm capable of picking up small objects. A preliminary analysis verifies the ability of the robot to acquire diverse knowledge from human-robot interaction.