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From real-time adaptation to social learning in robot ecosystems
Alex Szorkovszky1,2, Frank Veenstra1,2, Kyrre Glette1,2
1RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.
Frontiers in Robotics and AI
|October 20, 2023
Summary
This study introduces social learning for robots, enabling them to adapt quickly to environmental changes. Robots synchronize movements to learn new gaits, fostering diversity and potentially creating adaptable generalist robots.
Area of Science:
- Robotics
- Artificial Intelligence
- Evolutionary Computation
Background:
- Evolutionary robotics excels at creating adapted morphologies and controllers.
- Short-term environmental adaptation in robots is most efficiently achieved through learning.
- Existing robot learning methods often involve starting from scratch or using data from direct ancestors.
Purpose of the Study:
- To propose and demonstrate a novel social learning method for robot gait patterns.
- To leverage sensorimotor synchronization for inter-robot learning.
- To explore the potential for emergent generalist behaviors in robot populations.
Main Methods:
- Utilizing movement patterns of other robots as input for decentralized controllers like Central Pattern Generators (CPGs).
- Employing sensorimotor synchronization to drive CPGs into new, diverse limit cycles.
- Implementing a quasi-Hebbian feedback scheme to stabilize learned autonomous controllers.
Main Results:
- Demonstrated that movement patterns from other robots can induce novel gait patterns in decentralized controllers.
- Showcased the ability to lock in stable autonomous controllers through a quasi-Hebbian feedback mechanism.
- Provided a framework for social learning in robotic systems.
Conclusions:
- Social learning via sensorimotor synchronization offers an efficient adaptation mechanism for robots.
- This approach encourages movement diversity and can lead to the emergence of generalist task-solvers.
- The proposed method has implications for robot populations evolving in heterogeneous environments.
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