With the accelerated aging of the global population and escalating labor costs, more service robots are needed to help people perform complex tasks. As such, human-robot interaction is a particularly important research topic. To effectively transfer human behavior skills to a robot, in this study, we conveyed skill-learning functions via our proposed wearable device. The robotic teleoperation system utilizes interactive demonstration via the wearable device by directly controlling the speed of the motors. We present a rotation-invariant dynamical-movement-primitive method for learning interaction skills. We also conducted robotic teleoperation demonstrations and designed imitation learning experiments. The experimental human-robot interaction results confirm the effectiveness of the proposed method.

This paper focuses on multi-modal Information Perception (IP) for Soft Robotic Hands (SRHs) using Machine Learning (ML) algorithms. A flexible Optical Fiber-based Curvature Sensor (OFCS) is fabricated, consisting of a Light-Emitting Diode (LED), photosensitive detector, and optical fiber. Bending the roughened optical fiber generates lower light intensity, which reflecting the curvature of the soft finger. Together with the curvature and pressure information, multi-modal IP is performed to improve the recognition accuracy. Recognitions of gesture, object shape, size, and weight are implemented with multiple ML approaches, including the Supervised Learning Algorithms (SLAs) of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and the unSupervised Learning Algorithm (un-SLA) of K-Means Clustering (KMC). Moreover, Optical Sensor Information (OSI), Pressure Sensor Information (PSI), and Double-Sensor Information (DSI) are adopted to compare the recognition accuracies. The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective. The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations. Moreover, DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.