In the paper, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patients function limb (FL) to the impaired limb (IL). The IL mimics the action prescribed by the FL with the assistance of the wearable robot, stimulating and strengthening the injured muscles through repetitive exercise. A master-slave robotic system is presented to implement the mirror therapy. Especially, the reinforcement learning is involved in the human-robot interaction control to enhance the rehabilitation efficacy and guarantee safety. Multi-channel sensed information, including the motion trajectory, muscle activation and the users emotion, are incorporated in the learning algorithm. The muscle activation is expressed via the skin surface electromyography (EMG) signals, and the emotion is shown as the facial expression. The reinforcement learning approach is realized by the normalized advantage functions (NAF) algorithm. Then, a lower extremity rehabilitation robot with magnetorheological (MR) actuators is specially developed. The clinical experiments are carried out using the robot to verify the performance of the framework.

SUMMARY: A novel wall-climbing robot with multiple attachment modes is proposed. For uneven surfaces, the mechanical model of a spine wheel is brought out to grab the surfaces with its multi-spines. For smooth surfaces, an adhesive belt is obtained by the industrial synchronous belt and the polyurethane material to adhere to the surfaces. To avoid the robot overturning, an adsorption device with flexible skirt edge is presented. In addition, the normal force and motor torque are evaluated respectively. Finally, the prototype of the wall-climbing robot is manufactured and tested, and the experimental results show that the robot could climb the wall surface 0–360° with a maximum load of 0.5 kg.

Recognizing facial expression has attracted much more attention due to its broad range of applications in human–computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn–Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.

This article presents an innovative wall-climbing robot for detection on smooth wall surfaces, which consists of a vacuum adsorption system and adhesion belts, making the robot flexible and effectively steerable. Moreover, the detailed attachment mechanism is further analyzed for the climbing tasks. Safe operating conditions, kinematics, and dynamic model are derived, respectively, indicating that at least the adsorption force of 30 N and the motor torque of 2 N·m are required for stable climbing of the robot. Furthermore, the prototype of the wall-climbing robot is manufactured and the climbing abilities are tested on various wall surfaces showing that the maximum moving speed and corresponding load are 7.11 cm/s and 0.8 kg on the concrete exterior wall, 5.9 cm/s and 0.75 kg on the ceramic brick wall, 6.09 cm/s and 0.85 kg on the lime wall, and 5.9 cm/s and 1 kg on the acrylic surface, respectively, which demonstrates that the robot has high stability and adaptability.

In addition to high compliance to unstructured environments, soft robots can be further improved to gain the advantages of rigid robots by increasing stiffness. Indeed, realizing the adjustable stiffness of soft continuum robots can provide safer interactions with objects and greatly expand their application range. To address the above situation, we propose a tubular stiffening segment based on layer jamming. It can temporarily increase the stiffness of the soft robot in a desired configuration. Furthermore, we also present a spine-inspired soft robot that can provide support in tubular segments to prevent buckling. Theoretical analysis was conducted to predict the stiffness variation of the robot at different vacuum levels. Finally, we integrated the spine-inspired soft robot and tubular stiffening segment to obtain the tuneable-stiffness soft continuum robot (TSCR). Experimental tests were performed to evaluate the robot’s shape control and stiffness tuning effectiveness. Experimental results showed that the bending stiffness of the initial TSCR increased by more than 15× at 0°, 30× at 90°, and 60× in compressive stiffness.