  To support humans in their daily lives, robots are required to autonomously
learn, adapt to objects and environments, and perform the appropriate actions.
We tackled on the task of cooking scrambled eggs using real ingredients, in
which the robot needs to perceive the states of the egg and adjust stirring
movement in real time, while the egg is heated and the state changes
continuously. In previous works, handling changing objects was found to be
challenging because sensory information includes dynamical, both important or
noisy information, and the modality which should be focused on changes every
time, making it difficult to realize both perception and motion generation in
real time. We propose a predictive recurrent neural network with an attention
mechanism that can weigh the sensor input, distinguishing how important and
reliable each modality is, that realize quick and efficient perception and
motion generation. The model is trained with learning from the demonstration,
and allows the robot to acquire human-like skills. We validated the proposed
technique using the robot, Dry-AIREC, and with our learning model, it could
perform cooking eggs with unknown ingredients. The robot could change the
method of stirring and direction depending on the status of the egg, as in the
beginning it stirs in the whole pot, then subsequently, after the egg started
being heated, it starts flipping and splitting motion targeting specific areas,
although we did not explicitly indicate them.
