Understanding how animals produce a versatile locomotor repertoire requires unraveling the interplay between higher centers, decentralized locomotor circuits, and sensory feedback. However, the principles governing their integration remain elusive. We investigated amphibious centipedes through stepwise neural lesions and neuromechanical modeling. Behavioral experiments revealed that while decentralized circuits autonomously generate coordination, the brain and subesophageal ganglion provide situational flexibility, such as modulating trunk undulation and initiating leg folding. Integrating these findings, our model demonstrated how higher centers selectively inhibit or release lower circuit dynamics. Simulations verified that varying only a few descending control parameters reproduces transitions between slow walking, fast walking, and swimming. This work may capture the essence of the locomotor circuitry that harnesses decentralized self-organization to coordinate the body’s large degrees of freedom.
Hesam Azadjou, Suraj Chakravarthi Raja, Ali Marjaninejad, Francisco J. Valero‐Cuevas
Like mammals, robots must rapidly learn to control their bodies and interact with their environment despite incomplete knowledge of their body structure and surroundings. They must also adapt to continuous changes in both. This work presents a bio-inspired learning algorithm, General-to-Particular (G2P), applied to a tendon-driven quadruped robotic system developed and fabricated in-house. Our quadruped robot undergoes an initial five-minute phase of generalized motor babbling, followed by 15 refinement trials (each lasting 20 seconds) to achieve specific cyclical movements. This process mirrors the exploration-exploitation paradigm observed in mammals. With each refinement, the robot progressively improves upon its initial "good enough" solution. Our results serve as a proof-of-concept, demonstrating the hardware-in-the-loop system's ability to learn the control of a tendon-driven quadruped with redundancies in just a few minutes to achieve functional and adaptive cyclical non-convex movements. By advancing autonomous control in robotic locomotion, our approach paves the way for robots capable of dynamically adjusting to new environments, ensuring sustained adaptability and performance.