Achieving natural behavior in a robot using neurally inspired hierarchical perceptual control

Terrestrial locomotion presents tremendous computational challenges on account of the enormous degrees of freedom in legged animals, and complex, unpredictable properties of natural environments, including the body and its effectors, yet the nervous system can achieve locomotion with ease. Here we introduce a quadrupedal robot that is capable of posture control and goal-directed locomotion across uneven terrain. The control architecture is a hierarchical network of simple negative feedback control systems inspired by the organization of the vertebrate nervous system. This robot is capable of robust posture control and locomotion in novel environments with unpredictable disturbances. Unlike current robots, our robot does not use internal inverse and forward models, nor does it require any training in order to perform successfully in novel environments.

Duke Scholars

Author Henry Yin Psychology & Neuroscience