Versatile modular neural locomotion control with fast learning
This work addresses the problem of slow and opaque controller design for legged robots, offering a more efficient and versatile solution for roboticists, though it appears incremental in building upon modular and neural control concepts.
The authors tackled the challenge of designing locomotion control for legged robots by proposing a modular neural control structure that enables fast learning and incremental addition of behavior-specific modules, resulting in emergent adaptive behaviors for a hexapod robot in complex environments, with successful demonstration on a physical robot.
Legged robots have significant potential to operate in highly unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be either manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behavior-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviors, and that neural connections interfacing with existing modules can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviors allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the control approach was successfully demonstrated on a physical hexapod robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control for complex robotic systems.