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Action Selection and Execution in Everyday Activities: A Cognitive Robotics and Situation Model Perspective
David Vernon1, Josefine Albert2,3, Michael Beetz1
1Institute for Artificial Intelligence, University of Bremen.
Topics in Cognitive Science
|August 30, 2021
Summary
This study explores how cognitive robotics handles everyday tasks by analyzing complexity and transparency. The CRAM cognitive architecture successfully executes simple and complex activities using generalized action plans and modular knowledge.
Area of Science:
- Cognitive Robotics
- Artificial Intelligence
- Human-Robot Interaction
Background:
- Everyday activities require sophisticated cognitive mechanisms for robots.
- Understanding task complexity and transparency is crucial for robot task execution.
- Existing cognitive architectures may struggle with context-sensitive, flexible behavior.
Purpose of the Study:
- To examine the cognitive mechanisms for handling everyday activities in robotics.
- To differentiate tasks based on complexity and transparency.
- To extend cognitive architectures for improved context-sensitive behavior.
Main Methods:
- Analysis of task complexity (simple/complex) and transparency (easy/difficult).
- Utilizing the CRAM cognitive architecture for robot task execution.
- Employing generalized action plans with modular, composable knowledge chunks.
- Leveraging situation model perspective for future extensions.
Main Results:
- The CRAM architecture can perform simple and complex tasks like setting a table and loading a dishwasher.
- Generalized action plans transform underdetermined requests into successful motion plans.
- CRAM currently has limitations in handling difficult tasks.
Conclusions:
- Cognitive robotics can manage everyday activities through structured action plans and knowledge representation.
- The CRAM architecture provides a foundation for robot task execution.
- Further research is needed to enhance robot flexibility for difficult, context-sensitive tasks.
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