In this study, we introduce a novel MRI-compatible rotary series elastic actuator module utilizing velocity-sourced ultrasonic motors for force-controlled robots operating within MRI scanners. Unlike previous MRI-compatible SEA designs, our module incorporates a transmission force sensing series elastic actuator structure, with four off-the-shelf compression springs strategically placed between the gearbox housing and the motor housing. This design features a compact size, thus expanding possibilities for a wider range of MRI robotic applications. To achieve precise torque control, we develop a controller that incorporates a disturbance observer tailored for velocity-sourced motors. This controller enhances the robustness of torque control in our actuator module, even in the presence of varying external impedance, thereby augmenting its suitability for MRI-guided medical interventions. Experimental validation demonstrates the actuator's torque control performance in both 3 Tesla MRI and non-MRI environments, achieving a settling time of 0.1 seconds and a steady-state error within 2% of its maximum output torque. Notably, our force controller exhibits consistent performance across low and high external impedance scenarios, in contrast to conventional controllers for velocity-sourced series elastic actuators, which struggle with steady-state performance under low external impedance conditions.

Self-collision is also employed in the modern design of soft robots to enhance their performance during different tasks. However, developing an efficient and reliable simulator that can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collisionfree body of a soft robot can be obtained by minimizing the geometry-based objective function. Unlike the FEA-based Figure 1: Pneumatically actuated soft gripper that can effectively grasp physical simulation, the proposed pipeline performs a much objects by large inflation of chambers and the self-collision between lower computational cost. Moreover, adaptive remeshing is neighboring chambers: (a) the physical result on a soft gripper made applied to achieve the improvement of the convergence when by silicone casting and (b) our simulation result that can well predict dealing with soft robots that have large volume variations.