Intrinsically Motivated Learning in Natural and Artificial Systems

pp 279-300


Intrinsically Motivated Affordance Discovery and Modeling

  • Stephen HartAffiliated withManufacturing Systems Research Laboratory, General Motors R&D Email author 
  • , Roderic GrupenAffiliated withDepartment of Computer Science, University of Massachusetts Amherst

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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.