Morphology Choice Affects the Evolution of Affordance Detection in Robots
Federico Pigozzi, Stephanie Woodman, , Rebecca Kramer-Bottiglio, Josh Bongard
ACM Genetic and Evolutionary Computation Conference (GECCO), held in Lisbon (Portugal)
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A vital component of intelligent action is affordance detection: understanding what actions external objects afford the viewer. This requires the agent to understand the physical nature of the object being viewed, its own physical nature, and the potential relationships possible when they interact. Although robotics researchers have investigated affordance detection, the way in which the morphology of the robot facilitates, obstructs, or otherwise influences the robot’s ability to detect affordances has yet to be studied. We do so here and find that a robot with an appropriate morphology can evolve to predict whether it will fit through an aperture with just minimal tactile feedback. We also find that some robot morphologies facilitate the evolution of more accurate affordance detection, while others do not if all have the same evolutionary optimization budget. This work demonstrates that sensation, thought, and action are necessary but not sufficient for understanding how affordance detection may evolve in organisms or robots: morphology must also be taken into account. It also suggests that, in the future, we may optimize morphology along with control in order to facilitate affordance detection in robots, and thus improve their reliable and safe action in the world.