The Koopman operator framework has shown promising results in enabling the analysis of nonlinear dynamics into an infinite-dimensional linear representation. Koopman direct encoding (KDE) is a model-based approach that utilizes inner products and compositions in a Hilbert space to compute the Koopman operator. However, it has primarily been applied to autonomous systems and simulation environments. Here, we extend the application of KDE to nonautonomous systems and real-world environments by introducing Koopman direct encoding-based model predictive control (KDE-MPC). It was validated on nonlinear electromechanical systems with segmented dynamic conditions, such as contact-noncontact transitions, which pose challenges for modeling and control. Simulation results demonstrate a more stable and smoother position profile compared to proportional-integral-derivative control, particularly at discontinuous boundaries. KDE-MPC was also applied to real-world systems, achieving similar position tracking performance to simulation results. We anticipate that KDE-MPC will offer a viable solution for complex robotic control challenges.

The popularity of smart home devices has led to an increase in security incidents happening in smart homes. A key measure to avoid such incidents is to authenticate users before they can interact with smart devices. However, current methods often require additional hardware. This paper proposes, a gesture-based authentication system, an effective gesture-based authentication method built on top of the voice interfaces already available in these smart home devices, without adding new hardware. uses a gesture processing pipeline that identifies Doppler-existing frames and detects the Direction of Arrival of Reflection to authenticate users in low SNR environments and at longer distances. Furthermore, regarding the nature of gesture-based authentication, this system also supports detecting user liveness, preventing replay and synthesis attacks from remote attackers. The evaluation of shows high accuracy with a False Accept Rate (FAR) of 0.08% and False Reject Rate (FRR) of 3.10% for users within 1.5m of the device.

A lightweight and compliant manipulator design has been considered crucial in safe physical human–robot interaction. Remote actuation relocating the massive parts to the robot base and transmitting power to the distal joint minimizes the actuator inertia and provides series elasticity to the actuator. Rolling diaphragm hydrostatic transmission (RDHT), one of the remote actuation, has recently been studied in physically interacting robots, which can tackle the remaining issues in hydraulic actuation, such as low backdrivability and fluid leakage. However, existing RDHTs are challenging to achieve the desired safety and control performance simultaneously due to their fixed stiffness. This article presents a stiffness-switchable hydrostatic transmission (SwHST) consisting of an RDHT and valve-controlled pneumatic springs. The SwHST has a wide stiffness range of 15–290 N$\cdot$m/rad and a fast response in stiffness transition of less than 50 ms without any complex stiffness tuning mechanism. It is one of the most efficient transmissions in remote actuation and stiffness adjustment. Its static friction is less than 0.4% of full-range torque, and the stiffness-switching module consumes only 6 W of power when valves are open. The dynamic characteristics of the SwHST are experimentally scrutinized under various operational conditions. Safety performance is verified in unconstrained and constrained collision tests, demonstrating that the SwHST can effectively mitigate the clamping force of more than 50% for both the tests. Control performance is evaluated on position tracking tests. We foresee the proposed SwHST being utilized in human–robot collaboration without jeopardizing control performance through a rapid and efficient stiffness-switching mechanism.

Robots performing close physical interaction with humans would require a continuously variable transmission to operate in the region around the peak efficiency or peak power of the driving system. Conventional continuously variable transmission (CVT) has shown advantages in energy-efficient driving systems. However, these CVT designs are heavy and large for robotic applications. This paper presents a passively adaptable toroidal-CVT (pat-CVT) that is coupled with a twisted string actuator (TSA). The proposed combination of pat-CVT with TSA expands the operation range of TSA and mimics the torque-speed characteristics of artificial muscles. The contributions of the proposed system are as follows: 1) It has a high transmission ratio (4.6:1) compared to the level of existing CVT, and compact design; 2) We propose a structure that increases the power transmission efficiency by reducing slip rate during rotation through the application of soft material on the input/output disk and roller surfaces of the CVT; 3) Relations between the external load and the transmission ratio can be determined by selecting the spring in the system to optimize the motor operating conditions over the entire range of loads. We show theoretical modeling of pat-CVT with TSA and characterization of the transmission ratio, energy efficiency, and force-velocity curve.

Hydrostatic transmission has shown promising results for enabling the manipulator to achieve low effective inertia, high stiffness, and high torque density. However, the incompressibility of fluid causes the lack of compliance, so that it could not provide intrinsic safety. Thus, it would be advantageous to introduce series compliance on the hydrostatic manipulator for adjusting stiffness depending on the situation. Here, we developed a safe and high-performance hydrostatic actuator based on the switchable stiffness mechanism implemented with an air spring and solenoid valve. The hydrostatic transmission is implemented with rolling diaphragms to attain zero fluid leakage and low seal friction. Air spring is serially connected to hydraulic lines for achieving compliance. Its modes (i.e., stiff and compliant modes) can be rapidly switched by modulating water flow via the solenoid valve. Block stiffness experiment shows that the stiffness of stiff mode is 9.63 times stiffer than one of compliant mode at 100 kPa. We experimentally demonstrated that compliant mode could mitigate the impact force to the level that a tangerine would not be crushed. The stiffness was switched within 12 ms; hence it is fast enough to be used for feedback application with vision or tactile sensors. As a result, the developed actuator can ensure safety without sacrificing the dynamic performance, owing to the simple and rapidly switchable stiffness mechanism.

Electrical resistance tomography (ERT) has previously been utilized to develop a large-scale tactile sensor because this approach enables the estimation of the conductivity distribution among the electrodes based on a known physical model. Such a sensor made with a stretchable material can conform to a curved surface. However, this sensor cannot fully cover a cylindrical surface because in such a configuration, the edges of the sensor must meet each other. The electrode configuration becomes irregular in this edge region, which may degrade the sensor performance. In this paper, we introduce an ERT-based robotic skin with evenly and sparsely distributed electrodes. For implementation, we sprayed a carbon nanotube (CNT)-dispersed solution to form a conductive sensing domain on a cylindrical surface. The electrodes were firmly embedded in the surface so that the wires were not exposed to the outside. The sensor output images were estimated using a deep neural network (DNN), which was trained with noisy simulation data. An indentation experiment revealed that the localization error of the sensor was 5.2 ± 3.3 mm, which is remarkable performance with only 30 electrodes. A frame rate of up to 120 Hz could be achieved with a sensing domain area of 90 cm2. The proposed approach simplifies the fabrication of 3D-shaped sensors, allowing them to be easily applied to existing robot arms in a seamless and robust manner.