Search

Embodied artificial intelligence argues that the body and brain play equally important roles in the generation of adaptive behavior. An increasingly common approach therefore is to evolve an agent's morphology along with its control in the hope that evolution will find a good coupled system. In order for embodied artificial intelligence to gain credibility within the robotics and cognitive science communities, however, it is necessary to amass evidence not only for how to co-optimize morphology and control of adaptive machines, but why. This work provides two new lines of evidence for why this co-optimization is useful: Here we show that for an object manipulation task in which a simulated robot must accomplish one, two, or three objectives simultaneously, subjugating more aspects of the robot's morphology to selective pressure allows for the evolution of better robots as the number of objectives increases. In addition, for robots that successfully evolved to accomplish all of their objectives, those composed of evolved rather than fixed morphologies generalized better to previously unseen environmental conditions. [ABSTRACT FROM AUTHOR]

We propose a modular architecture for autonomous robots which allows for the implementation of basic behavioral modules by both programming and training, and accommodates for an evolutionary development of the interconnections among modules. This architecture can implement highly complex controllers and allows for incremental shaping of the robot behavior. Our proposal is exemplified and evaluated experimentally through a number of mobile robotic tasks involving exploration, battery recharging and object manipulation. [ABSTRACT FROM AUTHOR]