  Selection of appropriate tools and use of them when performing daily tasks is
a critical function for introducing robots for domestic applications. In
previous studies, however, adaptability to target objects was limited, making
it difficult to accordingly change tools and adjust actions. To manipulate
various objects with tools, robots must both understand tool functions and
recognize object characteristics to discern a tool-object-action relation. We
focus on active perception using multimodal sensorimotor data while a robot
interacts with objects, and allow the robot to recognize their extrinsic and
intrinsic characteristics. We construct a deep neural networks (DNN) model that
learns to recognize object characteristics, acquires tool-object-action
relations, and generates motions for tool selection and handling. As an example
tool-use situation, the robot performs an ingredients transfer task, using a
turner or ladle to transfer an ingredient from a pot to a bowl. The results
confirm that the robot recognizes object characteristics and servings even when
the target ingredients are unknown. We also examine the contributions of
images, force, and tactile data and show that learning a variety of multimodal
information results in rich perception for tool use.
