Developments in e-skins and machine learning may achieve tactile sensing and proprioception for autonomous, deployable soft robots.

Heterogeneous sensing mechanisms enabled a multifunctional compact soft sensor to decouple multiple deformation modes.

As robots begin to interact with humans and operate in human environments, safety becomes a major concern. Conventional robots, although reliable and consistent, can cause injury to anyone within its range of motion. Soft robotics, wherein systems are made to be soft and mechanically compliant, are thus a promising alternative due to their lightweight nature and ability to cushion impacts, but current designs often sacrifice accuracy and usefulness for safety. We, therefore, have developed a bioinspired robotic arm combining elements of rigid and soft robotics such that it exhibits the positive qualities of both, namely compliance and accuracy, while maintaining a low weight. This article describes the design of a robotic arm–wrist–hand system with seven degrees of freedom (DOFs). The shoulder and elbow each has two DOFs for two perpendicular rotational motions on each joint, and the hand has two DOFs for wrist rotations and one DOF for a grasp motion. The arm is pneumatically powered using custom-built McKibben type pneumatic artificial muscles, which are inflated and deflated using binary and proportional valves. The wrist and hand motions are actuated through servomotors. In addition to the actuators, the arm is equipped with a potentiometer in each joint for detecting joint angle changes. Simulation and experimental results for closed-loop position control are also presented in the article.

We propose a soft robotic ankle-foot-orthosis for poststroke patients, which is inexpensive, lightweight, easy to wear, and capable of gait assistance for rehabilitation not only in the clinic but also in daily life. The device includes a 3D-printed flexible brace and an ankle support that allows natural flexion and extension of the ankle but provides support in the vertical direction preventing the structure from buckling. A bi-directional tendon-driven actuator was used for assisting both dorsiflexion and plantarflexion. The device also contains a wearable gait sensing module for measuring the leg trajectory and the foot pressures in real time for feedback control. Since the device is powered by a rechargeable battery and communicates with the main controller wirelessly, it is fully untethered, making it mobile and comfortable. Using the measured sensor data and the biomechanics of the legs, the realtime gait phase is detected, and then a gait assistance algorithm for both dorsiflexion and plantarflexion provides an accurate prediction of a control phase and timing although there are variations in the gait trajectories among individuals. As a feasibility test, the walking experiment was conducted with a post-stroke patient. The result showed improvement in both gait propulsion and foot-drop prevention.

Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this paper were not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.

Soft sensors are becoming more popular in wearables as a means of tracking human body motions due to their high stretchability and easy wearability. However, previous research not only was limited to only certain body parts but also showed problems in both calibration and processing of the sensor signals, which are caused by the high nonlinearity and hysteresis of the soft materials and also by misplacement and displacement of the sensors during motion. Although this problem can be alleviated through redundancy by employing an increased number of sensors, it will lay another burden of heavy processing and power consumption. Moreover, complete full-body motion tracking has not been achieved yet. Therefore, we propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration of the soft sensor and estimation of the body motions. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on both the upper and the lower extremities. Three athletic motions were tested with a human subject, and the proposed learning-based calibration and mapping method showed a higher accuracy than traditional methods that are mainly based on mathematical estimation, such as linear regression.