  Recognizing human grasping strategies is an important factor in robot
teaching as these strategies contain the implicit knowledge necessary to
perform a series of manipulations smoothly. This study analyzed the effects of
object affordance-a prior distribution of grasp types for each object-on
convolutional neural network (CNN)-based grasp-type recognition. To this end,
we created datasets of first-person grasping-hand images labeled with grasp
types and object names, and tested a recognition pipeline leveraging object
affordance. We evaluated scenarios with real and illusory objects to be
grasped, to consider a teaching condition in mixed reality where the lack of
visual object information can make the CNN recognition challenging. The results
show that object affordance guided the CNN in both scenarios, increasing the
accuracy by 1) excluding unlikely grasp types from the candidates and 2)
enhancing likely grasp types. In addition, the "enhancing effect" was more
pronounced with high degrees of grasp-type heterogeneity. These results
indicate the effectiveness of object affordance for guiding grasp-type
recognition in robot teaching applications.
