Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Soft-bodied organisms demonstrate remarkable morphological adaptability, inspiring the development of diverse soft robot designs.
• Existing soft robot control frameworks struggle with rapid adaptation across different configurations, limiting their versatility.
• A unified control system for dynamically reconfiguring soft robots is needed to unlock their full potential.

Purpose of the Study:

• To introduce a generalizable control system enabling rapid cross-configuration adaptation in soft robots.
• To decouple control policies from specific robot morphologies for real-time, model-free adaptation.
• To establish a robust framework for diverse soft robot configurations.

Main Methods:

• Utilized reinforcement learning within a shared linear Koopman embedding space to encode robot dynamics.
• Developed a method to decouple control policies from specific morphologies by leveraging the Koopman embedding space.
• Validated the system across 33 distinct soft robot configurations.

Main Results:

• Achieved a 75x reduction in transfer samples needed for cross-configuration adaptation.
• Demonstrated robust performance under challenging conditions, including high-speed motion, heavy payloads, and multi-actuator faults.
• Enabled real-world skills previously unattainable in soft robotics.

Conclusions:

• The proposed adaptable control framework significantly enhances the versatility and performance of soft robots.
• This approach facilitates real-time, model-free policy adaptation without the need for extensive retraining.
• The findings offer insights into generalizable control strategies for complex physical systems.