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Using learned affordances for robotic behavior development

Doǧar, Mehmet R.
Ugur, Emre
Şahin, Erol
Çakmak, Maya
“Developmental robotics” proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. In this paper, we propose such a development scheme for a mobile robot. J.J. Gibson’s concept of “affordances” provides the basis of this development scheme, and we use a formalization of affordances to make the robot learn about the dynamics of its interactions with its environment. We show that an autonomous robot can start with pre-coded primitive behaviors, and as it executes its behaviors randomly in an environment, it can learn the affordance relations between the environment and its behaviors. We then present two ways of using these learned structures, in achieving more complex, intentional behaviors. In the first case, the robot still uses its pre-coded primitive behaviors only, but the sequencing of these primitive behaviors are such that new more complex behaviors emerge. In the second case, the robot makes a “blending” of its pre-coded primitive behaviors to create new behaviors that can be more effective in reaching its goal than any of the pre-coded behaviors.