Iterative affordance learning with adaptive action generation

摘要

A robot designer can provide a robot with knowledge to perform tasks on an environment. However, this approach can limit the achievement of future tasks executed by the robot. Providing it with the ability to develop its own skills paves the way for robots that are not limited by design. In this work a task consists in reproducing a given set of effects on an object. A robot must accomplish this task with limited information about the object, learning affordances to reproduce the effects, increasing this information throughout consecutive interactions with the object. We propose a method named Adaptive Affordance Learning (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L) which endows a robot with the capacity to learn affordances associated to an object, both adapting the robot's actions to the object position; and increases the robot's information about the object when needed. This paper presents two main contributions: first, an online adaption of the robot actions to interact with the object, decomposing each action into a sequence of movements, adapting each movement, in a close loop, to the object position; and second, to increase the information about the object, we propose an iterative process that alternates between (1) exploration of the environment interacting with the object, (2) affordance acquisition and (3) affordance validation. These contributions are assessed in two experiments where a simulated Baxter robot learns to push a box to different positions on a table.