Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning

Abstract

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks. The problem of motor coordination of complex multi-joint movements has been recognized as very difficult in biological as well as in technical systems. The high degree of redundancy of such movements and the complexity of their dynamics make it hard to arrive at robust solutions. Biological systems, however, are able to move with elegance and efficiency, and they have solved this problem by a combination of appropriate biomechanics, neuronal control, and adaptivity. Human walking is a prominent example of this, combining dynamic control with the physics of the body and letting it interact with the terrain in a highly energy-efficient way during walking or running. The current study is the first to use a similar hybrid and adaptive, mechano–neuronal design strategy to build and control a small, fast biped walking robot and to make it learn to adapt to changes in the terrain to a certain degree. This study thus presents a proof of concept for a design principle suggested by physiological findings and may help us to better understand the interplay of these different components in human walking as well as in other complex movement patterns.