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A psychology based approach for longitudinal development in cognitive robotics.
Frontiers in Neurorobotics
|January 31, 2014
Summary
Robots can learn new skills like human infants through intrinsic motivation and cumulative experience, advancing from basic movements to object manipulation. This developmental approach enables autonomous learning without pre-programming.
Area of Science:
- Robotics
- Developmental Psychology
- Artificial Intelligence
Background:
- Autonomous systems require adaptable learning capabilities for novel tasks.
- Current methods often rely on pre-programming or extensive training, limiting true autonomy.
- Developmental learning, inspired by human infants, offers a promising alternative for incremental skill acquisition.
Purpose of the Study:
- To present a developmental learning approach for robots inspired by human infant learning.
- To demonstrate longitudinal development in a humanoid robot (iCub).
- To investigate staged behavior, the role of constraints, bootstrapping, and schema memory in robotic learning.
Main Methods:
- Implemented a developmental learning framework on an iCub humanoid robot.
- Focused on intrinsic motivation and cumulative, incremental learning from environmental interaction.
- Utilized schema memory for experiential fragments to learn new skills.
Main Results:
- Observed rapid emergence of staged behavior during longitudinal development.
- Demonstrated the influence of constraints and bootstrapping between developmental stages.
- Showcased the robot's progression from motor babbling to integrated hand-eye reaching and object manipulation.
Conclusions:
- The infant-inspired developmental learning approach enables robots to acquire complex skills autonomously.
- This method facilitates fast and effective sensory-motor learning, reducing the need for pre-programming.
- Schema memory and intrinsic motivation are key components for cumulative skill acquisition in robotics.
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