In recent years, there has been an increase in the development of medical robots to enhance interventional MRI-guided therapies and operations. Magnetic resonance imaging (MRI) surgical robots are particularly attractive due to their ability to provide excellent soft-tissue contrast during these procedures. This paper describes a novel design for a tendon-driven gripper that utilizes four shape memory alloy (SMA) spring actuators and variable stiffness joints controlled by SMA coils for use in MRI surgical robot applications. The contact force of the gripper link is determined by the mechanical properties of the SMA spring actuators (SSA) and the angle of each linkage, and the joint stiffness can be adjusted by varying the electrical current applied to the SMA coil. To enhance the efficiency of the SSAs, a new cooling system using water has been proposed and implemented. To validate the effectiveness of our proposed gripper, we conducted three types of experiments, namely, a single SSA experiment, a single SMA coil experiment, and a whole gripper experiment. The experimental results demonstrate that the proposed water-cooling system can effectively solve temperature issues of SMA, and the joint stiffness in the austenite state is higher than that in the martensite state. Moreover, our experiments show that the presented gripper is capable of grasping and holding objects of various shapes and weights.

As the number of patients with amputations increases, research on assistive devices such as prosthetic limbs is actively being conducted. However, the development of assistive devices for patients with partial amputations is insufficient. In this study, we developed a finger prosthesis for patients with partial amputations. The design and mathematical modeling of the prosthesis are briefly presented. A pneumatic actuator, based on the McKibben muscle design, was employed to drive the finger prosthesis. We characterized the relationship between the actuator’s force and axial length changes with varying pressure. An empirical model derived from conventional mathematical modeling of force and axis length changes was proposed and compared with experimental data, and the error was measured to be between about 3% and 13%. In order to control the actuator using an electromyography (EMG) signal, an electrode was attached to the user’s finger flexors. The EMG signal was measured in relation to the actual gripping force and was provided with visual feedback, and the magnitude of the signal was evaluated using root mean square (RMS). Depending on the evaluated EMG signal magnitude, the pressure of the actuator was continuously adjusted. The pneumatic pressure was adjusted between 100 kPa and 250 kPa, and the gripping force of the finger prosthesis ranged from about 0.7 N to 6.5 N. The stiffness of the prosthesis can be varied using the SMA spring. The SMA spring is switched to a fully austenite state at 50 °C through PID control, and when the finger prosthesis is bent to a 90° angle, it can provide approximately 1.2 N of assistance force. Finally, the functional evaluation of the finger prosthesis was performed through a pinch grip test of eight movements.

Shape Memory Alloy (SMA) actuators offer strong potential for compact, lightweight, silent, and compliant robotic grippers; however, their practical deployment is limited by the challenge of controlling nonlinear and hysteretic thermal dynamics. This paper presents a complete Sim-to-Real control framework for precise temperature regulation of a tendon-driven SMA gripper using Deep Reinforcement Learning (DRL). A novel 12-action discrete control space is introduced, comprising 11 heating levels (0–100% PWM) and one active cooling action, enabling effective management of thermal inertia and environmental disturbances. The DRL agent is trained entirely in a calibrated thermo-mechanical simulation and deployed directly on physical hardware without real-world fine-tuning. Experimental results demonstrate accurate temperature tracking over a wide operating range (35–70 °C), achieving a mean steady-state error of approximately 0.26 °C below 50 °C and 0.41 °C at higher temperatures. Non-contact thermal imaging further confirms spatial temperature uniformity and the reliability of thermistor-based feedback. Finally, grasping experiments validate the practical effectiveness of the proposed controller, enabling reliable manipulation of delicate objects without crushing or slippage. These results demonstrate that the proposed DRL-based Sim-to-Real framework provides a robust and practical solution for high-precision SMA temperature control in soft robotic systems.