  We propose a tool-use model that can detect the features of tools, target
objects, and actions from the provided effects of object manipulation. We
construct a model that enables robots to manipulate objects with tools, using
infant learning as a concept. To realize this, we train sensory-motor data
recorded during a tool-use task performed by a robot with deep learning.
Experiments include four factors: (1) tools, (2) objects, (3) actions, and (4)
effects, which the model considers simultaneously. For evaluation, the robot
generates predicted images and motions given information of the effects of
using unknown tools and objects. We confirm that the robot is capable of
detecting features of tools, objects, and actions by learning the effects and
executing the task.
