Autonomous learning of action-word semantics in a humanoid robot

For creation of an artificial agent that is capable of using language naturally, models that only manipulate symbols or classify speech are ineffective. The semantic information which language conveys must be grounded in the agent’s complete sensorimotor experience. Typically, patterns from visual, auditory, and proprioceptive data streams which share the same conceptual cause are fused together in an associative memory at the core of the language model. Coupling of motor and auditory modalities, which is crucial for a large part of semantic understanding, presents a particularly difficult challenge. Words and actions both need models capable of capturing spatial and temporal structure, and training algorithms that can learn in a self-organizing, incremental fashion. Presented is a method for online learning of word and action lexicons based on the hidden Markov model. The model is then evaluated through action-word learning experiments implemented on a humanoid robot.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Niehaus, Logan

Contributors dc:contributor

• Levinson, Stephen E.

• language acquisition
• autonomous mental development
• cognitive robotics
• embodied cognition

Statement dc:rights

• Copyright 2011 Logan Niehaus

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/24151

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/24151