  The current study investigated possible human-robot kinaesthetic interaction
using a variational recurrent neural network model, called PV-RNN, which is
based on the free energy principle. Our prior robotic studies using PV-RNN
showed that the nature of interactions between top-down expectation and
bottom-up inference is strongly affected by a parameter, called the meta-prior,
which regulates the complexity term in free energy.The study also compares the
counter force generated when trained transitions are induced by a human
experimenter and when untrained transitions are induced. Our experimental
results indicated that (1) the human experimenter needs more/less force to
induce trained transitions when $w$ is set with larger/smaller values, (2) the
human experimenter needs more force to act on the robot when he attempts to
induce untrained as opposed to trained movement pattern transitions. Our
analysis of time development of essential variables and values in PV-RNN during
bodily interaction clarified the mechanism by which gaps in actional intentions
between the human experimenter and the robot can be manifested as reaction
forces between them.
