Intrinsically Motivated Affordance Discovery and Modeling


In this chapter, we argue that a single intrinsic motivation function for affordance discovery can guide long-term learning in robot systems. To these ends, we provide a novel definition of “affordance” as the latent potential for the closed-loop control of environmental stimuli perceived by sensors. Specifically, the proposed intrinsic motivation function rewards the discovery of such control affordances. We will demonstrate how this function has been used by a humanoid robot to learn a number of general purpose control skills that address many different tasks. These skills, for example, include strategies for finding, grasping, and placing simple objects. We further show how this same intrinsic reward function is used to direct the robot to build stable models of when the environment affords these skills.

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@inproceedings{Hart2013IntrinsicallyMA, title={Intrinsically Motivated Affordance Discovery and Modeling}, author={Stephen Hart and Roderic A. Grupen}, year={2013} }