In recent years, significant progress has been made in functional soft materials, alongside advances in nano/micromanufacturing techniques, driving the evolution of soft grippers to the forefront of robotics innovation. Compared to their traditional rigid counterparts, soft grippers offer unparalleled adaptability, effortlessly conforming to objects of varying sizes and shapes. This comprehensive review explores the latest trends shaping the landscape of soft robotic grippers, providing insights into their diverse functionalities and applications. The exploration begins with an examination of the various actuation mechanisms utilized by soft grippers, including cable or tendon-driven, pneumatic, electroactive, and thermoactive systems. Additionally, the review delves into the intricacies of grasping and manipulating mechanisms, spanning from multi-finger configurations to innovative approaches, such as jamming, suction, and adhesion grasping. Notably, hybrid grippers, which integrate multiple actuation and grasping mechanisms, are of particular interest, thereby enhancing the range of functionalities offered by these grippers. Finally, the review briefly addresses current limitations and future directions in the field.

Hands perform various functions. There are many inconveniences in life without the use of hands. People without the use of hands wear prostheses. Recently, there have been many developments and studies about robotic prosthetic hands performing hand functions. Grasping motions of robotic prosthetic hands are integral in performing various functions. Grasping motions of robotic prosthetic hands are required recognition of grasping targets. A path toward using images to recognize grasping targets exists. In this study, object recognition in images for grasping motions are performed by using object detection based on deep-learning. A suitable model for the grasping motion was examined through three object detection models. Also, we present a method for selecting a grasping target when several objects are recognized. Additionally, it will be used for grasping control of robotic prosthetic hands in the future and possibly enable automatic control robotic prosthetic hands.

Conventional prosthetic hands require users to activate designated muscles or press buttons to select among predefined grasping patterns. These methods are time-consuming and increase muscle fatigue. This study proposes a regression model that differentiates multiple muscle activation patterns allowing the user to select a desired grasping pattern. We classified four hand primitives and three force intensities, which can reflect the intention of prosthetic hand users. An 8-channel band-type sEMG sensor was used to measure myoelectric signals from an amputated upper-arm. To acquire the sEMG data, the amputee was instructed to imagine four hand primitives (fist, open hand, flexion, and extension) with three levels of force intensity (low, medium, and high). Time-domain features (mean average value, variance, waveform length, and root mean square) were extracted from the sEMG signal and classified using a Support Vector Machine. The hand primitives and force intensities had accuracies of 95% and 90%, respectively. Results indicate the regression model reflected the user’s intention to select different grasping patterns, and is thus expected to improve the quality of life of amputees.