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Integrated Cognitive Architecture for Robot Learning of Action and Language
Kazuki Miyazawa1, Takato Horii1, Tatsuya Aoki1,2
1Graduate School of Engineering Science, Osaka University, Osaka, Japan.
Frontiers in Robotics and AI
|January 27, 2021
Summary
This study introduces a novel AI framework enabling robots to understand concepts, actions, and language simultaneously. This integration allows robots to learn, plan, and act based on comprehension, advancing cognitive robotics.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- High-level cognitive functions like action planning and language understanding remain challenging for current robotics.
- Implementing human-like learning, planning, and decision-making in robots is a significant research goal.
Purpose of the Study:
- To propose a framework for the simultaneous comprehension of concepts, actions, and language in robots.
- To bridge the gap between current robotic capabilities and advanced cognitive functions.
Main Methods:
- Integration of cognitive modules using multilayered multimodal latent Dirichlet allocation (mMLDA) for multimodal categorization.
- Combining reinforcement learning with mMLDA for understanding-based actions.
- Utilizing mMLDA with grammar learning and Bayesian hidden Markov models (BHMM) for verbalization and utterance comprehension.
Main Results:
- Demonstrated a framework for robots to process and integrate multimodal information (concepts, actions, language).
- Enabled robots to perform actions based on comprehension and understanding of their environment.
- Facilitated robots in verbalizing their actions and understanding human language input.
Conclusions:
- The proposed architecture shows potential for advancing cognitive robotics by enabling robots to comprehend and interact using concepts, actions, and language.
- This framework represents a significant step towards creating more intelligent and human-like robots capable of complex cognitive tasks.
Keywords:
cognitive architectureconcept formationgenerative modellanguage learningmultimodal categorizationreinforcement learningsystem integrationMore Related Videos
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