An affordance is a relation between an object, an action, and the effect of that action in a given environmental context.
One key benefit of the concept of affordance is that it provides information about the consequence of an action which can be stored and reused in a range of tasks that a robot needs to learn and perform. In this paper, we address the challenge of the on-line learning and use of affordances simultaneously while performing goal-directed tasks. This requires efficient on-line performance to ensure the robot is able to achieve its goal fast. By providing conceptual knowledge of action possibilities and desired effects, we show that a humanoid robot NAO can learn and use affordances in two different task settings. We demonstrate the effectiveness of this approach by integrating affordances into an Extended Classifier System for learning general rules in a reinforcement learning framework. Our experimental results show significant speedups in learning how a robot solves a given task.
For more information, please refer to our IROS paper:
Chang Wang, Koen V. Hindriks, and Robert Babuska. “Robot Learning and Use of Affordances in Goal-directed Tasks.”, IROS 2013. In Press.
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