Summary

This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. It will be reviewed how research on representations of, and functional connections between action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning will also be described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted and an overview of current project

Bio-inspired walking for humanoid robots using feet with human-like compliance and neuromuscular control

The human foot plays a key role in human walking providing, among others, body support and propulsion, stability of the movement and impact absorption. These fundamental functionalities are accomplished by an extraordinarily rich bio-mechanical design. Nonetheless, humanoid robots follow different approaches to walk, hence, they generally implement rigid feet. In this study, we target the gap existing between the human foot and traditional humanoid-robot feet. More specifically, we evaluate the resulting advantages and draw-backs by implementing on a humanoid robot some of the properties and functionalities embedded in the human foot. To this end, we extract the physical characteristics of a prosthetic foot to develop a human-like foot model. This foot model is systematically tested in simulation in human-like walking tasks on flat ground and on uneven terrain. The movement of the limbs is generated by a muscle-reflex controller based on a simplified model of the human limbs. The gait features and the walking stability are evaluated for the human-like foot and compared with the results produced using rigid feet