  Object affordance is an important concept in human-object interaction,
providing information on action possibilities based on human motor capacity and
objects' physical property thus benefiting tasks such as action anticipation
and robot imitation learning. However, existing datasets often: 1) mix up
affordance with object functionality; 2) confuse affordance with goal-related
action; and 3) ignore human motor capacity. This paper proposes an efficient
annotation scheme to address these issues by combining goal-irrelevant motor
actions and grasp types as affordance labels and introducing the concept of
mechanical action to represent the action possibilities between two objects. We
provide new annotations by applying this scheme to the EPIC-KITCHENS dataset
and test our annotation with tasks such as affordance recognition. We
qualitatively verify that models trained with our annotation can distinguish
affordance and mechanical actions.
