Human-Robot Kinaesthetic Interactions Based on the Free-Energy Principle
本研究使用基于自由能原理的变分递归神经网络模型PV-RNN，探讨了人类实验者与机器人之间的运动交互，发现调节模型参数会影响交互中的反作用力，且机器人能区分训练过和未训练过的运动模式。
The current study investigated possible human-robot kinaesthetic interactions using a variational recurrent neural network (RNN) 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 the 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 current study examines how changing the meta-prior w in the interaction phase affects the counter force generated when an experimenter attempts to induce movement pattern transitions familiar to the robot through its prior training. The study also compares the counter force generated when trained transitions are induced by a human experimenter and when untrained transitions are induced. Finally, the study examines how different levels of disturbances, pattern level, and cognitive level can be resolved internally across different layers. 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; and 3) when the robot was disturbed in the cognitive level, the disturbance was resolved more in the higher layer as compared to the case with disturbance in the pattern level. Our analysis of time development of essential variables and values in PV-RNN during bodily interaction clarified the mechanism by which gaps in action intentions between the human experimenter and the robot can be manifested as reaction forces between them.