  Various robots have been developed so far; however, we face challenges in
modeling the low-rigidity bodies of some robots. In particular, the deflection
of the body changes during tool-use due to object grasping, resulting in
significant shifts in the tool-tip position and the body's center of gravity.
Moreover, this deflection varies depending on the weight and length of the
tool, making these models exceptionally complex. However, there is currently no
control or learning method that takes all of these effects into account. In
this study, we propose a method for constructing a neural network that
describes the mutual relationship among joint angle, visual information, and
tactile information from the feet. We aim to train this network using the
actual robot data and utilize it for tool-tip control. Additionally, we employ
Parametric Bias to capture changes in this mutual relationship caused by
variations in the weight and length of tools, enabling us to understand the
characteristics of the grasped tool from the current sensor information. We
apply this approach to the whole-body tool-use on KXR, a low-rigidity
plastic-made humanoid robot, to validate its effectiveness.
