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.

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Compared with traditional rigid gripper with joint-linkage structure, novel soft robotic gripper gives rise to continuous concern for the advantages of no-damage grasping, convenient manufacture, easy control, and low cost. In this study, we design and built two kinds of soft robotic grippers with four fiber-reinforced soft actuators which are distributed in circular and rectangle shapes for single and twin contacts grasping. A novel hybrid valve pneumatic control scheme combining proportional and solenoid valves is proposed. Also, a mode controllable hybrid valve pressure control method is proposed to adjust internal pressure of soft robotic grippers to adapt to different grasping tasks. The experiment results verify that the performances of hybrid valve outperform those of individual proportional valve or solenoid valve in the aspects of response time and steady-state accuracy. The hybrid valve has wide range of pressure regulation, result in that the soft robotic grippers are qualified to grasp various objects with different shapes, sizes, and weights.

A novel variable stiffness soft robotic hand (SRH) consists of three pieces of layer jamming structure (LJS) is proposed. The mechanism is driven by the motor-based tendon along the surface of the pieces that connect to individual gas channel. Each LJS is optimised by adhering a thin layer of hot melt adhesive and overlapping the spring steel sheet as inner layer material. It can be switched between rigid and compliant independently. The structures of variable stiffness and tendondriven lead to various deformation poses. Then the control system of SRH and the performance analysis of the LJS are introduced. Finally, the experiments are implemented to prove the superiority of the proposed LJS and the demonstrations show that the designed robotic hand has multiple configurations to successfully grasp various objects.

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