Recent advancements in soft robotics have seen the rapid development of soft grippers for industrial pick-and-place applications. They are however ill-suited to bear heavy loads due to their compliant nature. Paradoxically, researchers have sought to increase the stiffness of soft grippers to improve loadbearing capabilities. Unfortunately, contemporary soft actuators with variable stiffness are fabricated using manual processes and their performance is subject to an individual's mastery. They are hence not reliable for long-term industrial use. In this paper, we present our work on a 3D-printed metalendoskeleton-reinforced actuator (MERA) for industrial pickand-place applications. We also highlight the fabrication processes needed to recreate it repetitively. Using stainless steel splints (SSS), we demonstrate that MERA is able to modulate its stiffness at selective junctures for stable and effective grasping. We also describe our design rationale with a qualitative mathematical model and validate its performance quantitatively using a finite element model, which is further investigated in the following fatigue test. In our experiments, the MERA equipped with SSS is able to output a peak tip force of 8N, which is a 291% increase compared to the one without metallic reinforcement. In addition, an increase of 76.5% in gripping load and a maximum holding force per actuator of 13.8N are realized through the stiffness tuning of a MERA-Gripper. Despite significantly improving loadbearing capabilities, the actuator manages to retain an overall low profile with a weight of 82g. Finally, we adapted the MERA into a reconfigurable gripper and tested its grasping capabilities on objects of various shapes, sizes, and weights.

Loss of functional motor skills are common and often require patients to undergo rehabilitation so that they have a chance at motor recovery. Advancement in technology has seen to a rise in the use of robotic technology in conducting rehabilitative exercises that are traditionally carried out by physiotherapists. In recent years, soft robotic exoskeletons, using pneumatic-based actuation in particular, have gained much interest due to their compliant characteristics and safe operating conditions. In order to carry out complex task-based rehabilitative exercises, these soft pneumatic actuators must ideally be able to move with multiple degrees of freedom or minimally, in a bidirectional motion. Majority of the research covering soft actuators can only achieve finger flexion with some providing passive finger extension. Non-invasive intent detection in the control of these exoskeletons is also lacking in sensing both finger flexion and extension. In this paper we present our work on a fold-based bidirectional 3D printed intent-sensing soft pneumatic actuator (ISPA) that can achieve bidirectional motion and provide intent detection for finger flexion and extension for application in upper limb rehabilitative exoskeletons.

This paper presents the design and preliminary investigation of a fully 3D printed soft robotic hand exoskeleton, Print-it-Yourself (PIY) Glove for stroke patients. The PIY Glove is fabricated with Fused Deposition Modeling (FDM) using consumer based 3D printing technology to lower fabrication costs and allow patients to 3D print a rehabilitative and assistive device at home. PIY Glove uses a novel, fold based design of 3D printed soft actuators to achieve bending motion in the fingers. Fabrication guidelines of the PIY Glove are laid out and characterization of the glove in terms of its range of motion and grip force are also presented. An EMG control system that achieves control of the PIY Glove is also described. This work paves the way for the implementation of printable pneumatics in real-world applications, particularly robot-assisted hand therapy.

This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.