Researches in the brain science field have uncovered the human capability to use tools as if they are part of the human bodies (known as tool–body assimilation) through trial and experience. This paper presents a method to apply a robot's active sensing experience to create the tool–body assimilation model. The model is composed of a feature extraction module, dynamics learning module, and a tool–body assimilation module. Self-organizing map (SOM) is used for the feature extraction module to extract object features from raw images. Multiple time-scales recurrent neural network (MTRNN) is used as the dynamics learning module. Parametric bias (PB) nodes are attached to the weights of MTRNN as second-order network to modulate the behavior of MTRNN based on the properties of the tool. The generalization capability of neural networks provide the model the ability to deal with unknown tools. Experiments were conducted with the humanoid robot HRP-2 using no tool, I-shaped, T-shaped, and L-shaped tools. The distribution of PB values have shown that the model has learned that the robot's dynamic properties change when holding a tool. Motion generation experiments show that the tool–body assimilation model is capable of applying to unknown tools to generate goal-oriented motions.
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