The concept of morphological computation holds that the body of an agent can, under certain circumstances, exploit the interaction with the environment to achieve useful behavior, potentially reducing the computational burden of the brain/controller. The conditions under which such phenomenon arises are, however, unclear. We hypothesize that morphological computation will be facilitated by body plans with appropriate geometric, material, and growth properties, while it will be hindered by other body plans in which one or more of these three properties is not well suited to the task. We test this by evolving the geometries and growth processes of soft robots, with either manually-set softer or stiffer material properties. Results support our hypothesis: we find that for the task investigated, evolved softer robots achieve better performances with simpler growth processes than evolved stiffer ones. We hold that the softer robots succeed because they are better able to exploit morphological computation. This four-way interaction among geometry, growth, material properties and morphological computation is but one example phenomenon that can be investigated using the system here introduced, that could enable future studies on the evolution and development of generic soft-bodied creatures.

The Web has created new opportunities for interactive problem solving and design by large groups. In the context of robotics, we have shown recently that a crowd of non-experts are capable of designing adaptive machines over the Web. However, determining the degree to which collective contribution plays a part in these tasks requires further investigation. We hypothesize that there exist subtle yet measurable social dynamics that occur during the collaborative design of robots on the Web. To test this, we enabled a crowd to rapidly design and train simulated, web-embedded robots. We compared the robots designed by a socially-interacting group of individuals to another group whose members were isolated from one another. We found that there exists a latent quality in the robots designed by the social group that was significantly less prevalent in the robots designed by individuals working alone. Thus, there must exist synergies in the former group that facilitate this design task. We also show that this latent quantity correlates with the desired design outcome, which was fast forward locomotion. However, the quantity when distilled into its component parts is not more prevalent in one group than another. This finding demonstrates that there are indeed traces left behind in the machines designed by the crowd that betray the social dynamics that gave rise to them. Demonstrating the existence of such quantities and the methodology for extracting them presents opportunities for crafting interfaces to magnify these synergies and thus improve collective design of robots over the web in particular, and crowd design activities in general.