  In the near future, robots are expected to work with humans or operate alone
and may replace human workers in various fields such as homes and factories. In
a previous study, we proposed bilateral control-based imitation learning that
enables robots to utilize force information and operate almost simultaneously
with an expert's demonstration. In addition, we recently proposed an
autoregressive neural network model (SM2SM) for bilateral control-based
imitation learning to obtain long-term inferences. In the SM2SM model, both
master and slave states must be input, but the master states are obtained from
the previous outputs of the SM2SM model, resulting in destabilized estimation
under large environmental variations. Hence, a new autoregressive neural
network model (S2SM) is proposed in this study. This model requires only the
slave state as input and its outputs are the next slave and master states,
thereby improving the task success rates. In addition, a new feedback
controller that utilizes the error between the responses and estimates of the
slave is proposed, which shows better reproducibility.
