Moving in an Uncertain World: Robust and Adaptive Control of Locomotion from Organisms to Machine Intelligence

Area of Science:

• Biophysics and biomechanics
• Control theory and robotics
• Animal locomotion and behavior

Background:

• Organisms demonstrate remarkable locomotion capabilities in complex, uncertain environments.
• Animals have evolved sophisticated mechanisms to manage internal and external uncertainties for sustained performance.
• Understanding these mechanisms is crucial for advancing bio-inspired robotics and control systems.

Purpose of the Study:

• To present mathematical engineering methods for analyzing robust and adaptive control of organismal locomotion.
• To decompose the hierarchical structure of locomotor control along a robust-adaptive axis.
• To provide testable hypotheses for classifying behavioral responses to perturbations.

Main Methods:

• Application of control theory frameworks to model locomotor control.
• Decomposition of control systems into robust and adaptive components.
• Analysis of behavioral data from non-human animals, focusing on appendage loss and image stabilization.

Main Results:

• A hierarchical structure of robust and adaptive locomotor control has been identified.
• The robust-adaptive axis offers a framework for classifying behavioral responses to perturbations.
• Examples of robust and adaptive locomotion are presented for appendage loss and image stabilization behaviors.

Conclusions:

• Mathematical engineering approaches provide valuable insights into the principles of robust and adaptive locomotion.
• Mapping these principles across animal groups and bio-inspired robots highlights commonalities and differences.
• Future interdisciplinary research is essential to fully unravel the complexities of organismal locomotion control.