THREE GENERATIONS OF COEVOLUTIONARY ROBOTICS

The field of robotics today faces a practical economic problem: flexible machines with minds cost so much more than manual machines and their humans operators. Few would spend $2000 on a vacuum cleaner when a manual one is $200, or half a million dollars on a driverless car when a regular car is $20,000, plus $6 per hour for its driver. The high costs associated with designing, building, and controlling robots have led to a stasis [1], and robots in industry are only applied to simple and highly repetitive manufacturing tasks. Even though sophisticated teleoperated machines with sensors and actuators have found important applications (exploration of inaccessible environments, for example), they leave very little decision, if at all, to the on-board software [2]. The central issue addressed by our work is a low-cost way to get a higher level of complex physicality under control. We seek more controlled and moving mechanical parts, more sensors, more nonlinear interacting degrees of freedom, without entailing both the huge fixed costs of human design and programming, and the variable costs in manufacture and operation. We suggest that this can be achieved only when robot design and construction are fully automatic, and the results are inexpensive enough to be disposable. Traditionally, robots are designed on a disciplinary basis: mechanical engineers design complex articulated bodies, with state-of-the-art sensors, actuators, and multiple degrees of freedom. These elaborate machines are then thrown over the wall to the control department, where software programmers and control engineers struggle to design a suitable controller. Even if an intelligent human can learn to control such a device, it does not follow that automatic autonomous control can be had at any price. Humans drastically underestimate animal brains: looking into nature, we