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.

翻译：本研究利用一种基于自由能原理的变分递归神经网络模型——PV-RNN，探讨了可能的人机动觉交互。我们此前使用PV-RNN的机器人研究表明，自上而下的期望与自下而上的推理之间的交互性质受到一个称为“元先验”参数的强烈影响，该参数调节了自由能中的复杂度项。本研究还比较了当由人类实验者诱导经过训练的运动转化与未经过训练的运动转化时所产生的反作用力。我们的实验结果表明：（1）当 $w$ 设置为较大/较小值时，人类实验者需要更多/更少的力来诱导经过训练的运动转化；（2）当人类实验者试图诱导未经过训练的运动模式转化（相对于经过训练的运动模式转化）时，需要施加更大的力作用在机器人上。我们对身体交互过程中PV-RNN关键变量和值的时间演化分析，阐明了人类实验者与机器人之间动作意图差距如何表现为它们之间的反作用力的机制。