Affordance triggering for arbitrary states based on robot exploring

An affordance-based method is a biology-inspired way to promote the cognitive capabilities of robots. Affordances encode the relationships between a robot and environment in terms of actions that the robot is able to perform. The most notable feature of affordance-based perception is that an object is perceived by its functional attributes (e.g., reachable, graspable, etc) instead of its visual attributes (e.g., size, shape, etc). Most existing works treat affordance prediction as a binary classification problem and assume that an affordance never hides. However, sometimes the robot needs to adapt itself, i.e., rotate its wrist or turn left, to trigger the hidden affordance because the interaction point is very important and the object might be placed in any state. In this paper, we consider affordance triggering for arbitrary states as a regression problem. In the experiment, a manipulative robot could rotate its wrist to find the graspable affordance regardless the state of an object.

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