Precise kinematic modeling and control of soft continuum robots are challenged by their inherent deformability and complex interactions with the environment. This paper proposes an adaptive kinematic modeling framework based on a Deep Belief Network with Event-Driven Incremental Learning, designed for artificial intelligence-enabled control of soft robotic systems. The deep belief network is first pre-trained using simulated data to establish an initial kinematic model, requiring only approximately 600 real-world samples for deployment. An event-driven incremental learning mechanism is then introduced to adapt the model online. This mechanism is guided by a spike intensity metric, which evaluates prediction errors and selectively triggers either fine-tuning of network parameters or the integration of new radial basis function nodes to compensate for unmodeled kinematic effects and environmental disturbances. The adaptive kinematic model is embedded within a closed-loop controller to achieve accurate trajectory tracking. Experimental validation is conducted on a tendon-driven soft origami manipulator, covering trajectory tracking under varying payloads and deformation constraints, disturbance rejection, and teleoperation tasks. The proposed framework achieves sub-millimeter average trajectory tracking accuracy under confined conditions and outperforms baseline deep belief network models and piecewise constant curvature approaches. The results demonstrate that the proposed artificial intelligence-based adaptive kinematic modeling method provides a data-efficient and robust solution for precise control of soft continuum robots in applications such as medical robotics and flexible manufacturing.

Soft manipulators offer the advantages of safety and adaptability. However, due to insufficient stiffness and single motion mode limitations, existing soft manipulators usually exhibit low load capacity and small working space. To address this problem, we propose a novel soft hybrid-driven manipulator with continuous stiffness control capability and multiple motion patterns (omnidirectional bending and extension). Furthermore, we develop kinematic and stiffness models based on the constant curvature assumption. The soft robot consists of a soft bellows actuator and inextensible rigid skeletons, which exhibit a high extension ratio and low drive pressure. With the antagonistic actuation of tendon-pulling and air-pushing, the robot can achieve independent control over stiffness and position in three-dimensional space. The performance associated with the designed soft hybrid-driven manipulator is experimentally verified. The robot can achieve an elongation of 198% and a maximum bending angle of up to 240°. The robot can also increase stiffness by increasing internal air pressure to resist deformation caused by external loads. Additionally, tracking experiments with various trajectories in space verify the accuracy of the kinematic model, which indicates that the soft manipulator can stabilize motion within a broad workspace.

In order to meet the mobility and physical activity needs of people with impaired limbs function, a novel limbs-free variable structure wheelchair system controled by face-computer interface (FCI) was developed in this study. FCI used facial electromyography (fEMG) as a human intention recognition method from 6 facial movements, and the accuracy of intent recognition reached 97.6% under a series of offline optimization including channel optimization based on the Hilbert transform to obtain the envelope of fEMG, features optimization, and channel-independent model optimization. A collection of finite state machines (FSM) was used to control the movement and structural changes of the wheelchair. A shared control strategy called “ Keep Action after Take Over (KAaTO) “ that can reduce user fatigue while increasing safety was used in long-distance movement control of wheelchair. To test the performance of the system, in the braking distance test experiment, the result of 0.429m under KAaTO was better than the EMG-based discrete command control and speech command control method. Finally, an outdoor long-distance control pilot experiment proved the superior performance of the developed system.

In sEMG-based recognition systems, accuracy is severely worsened by disturbances, such as electrode shifts by doffing/donning. Traditional recognition models are fixed or static, with limited abilities to work in the presence of the disturbances. In this paper, a transfer learning method is proposed to reduce the impact of electrode shifts. In the proposed method, a novel activation angle is introduced to locate electrodes within a polar coordinate system. An adaptive transformation is utilized to correct electrode-shifted sEMG samples. The transformation is based on estimated shifts relative to the initial position. The experiments acquisition data from ten subjects consist of sEMG signals under eight gestures in seven or nine arbitrary positions, and recorded shifts from a 3D-printed annular ruler. In our extensive experiments, the errors between recorded shifts (as the reference) and estimated shifts is about -0.017±0.13 radians. Eight gestures recognition results have shown an average accuracy around 79.32%, which represents a significant improvement over the 35.72% ( ) average accuracy of results obtained using nonadaptive models, and 60.99% ( ) results of the other method iGLCM (an improved gray-level co-occurrence matrix). More importantly, by only using one-label samples, the proposed method updates the pre-trained model in an initial position. As a result, the pre-trained model can be adaptively corrected to recognize eight-label gestures in arbitrarily rotary positions. It is proven a highly efficient way to relieve subjects' re-training burden of sEMG-based rehabilitation systems.

A multifunctional myoelectric prosthetic hand is a perfect gift for an upper-limb amputee, however, the myoelectric control for a prosthetic hand is not so good now. Here, the paper presents a comparative study on electromyography (EMG) pattern recognition based on PCA and LDA for an anthropomorphic robotic hand. Four channels of surface EMG (sEMG) signals were recorded from the subject's forearm. Time-domain analysis, frequency-domain analysis, wavelet transform analysis, nonlinear entropy analysis and fractal analysis were done and fourteen kinds of features were extracted from sEMG signals. The features were divided into four groups, and the performances of the four groups were compared and analyzed. In the feature projection stage, three schemes were proposed and their performances were compared with each other. The first one only used the principal component analysis (PCA) for dimension reduction. And the second one only used the linear discriminant analysis (LDA) for dimension reduction. The third one used PCA for the first step of dimensionality reduction, and then used LDA for the next step of dimensionality reduction. In the classification stage, minimum distance classifier (MDC) was employed for identifying nine kinds of hand/wrist motions in the projected space. Comparative experiments of four groups of features and three projection schemes were done and evaluated. The online experiment of real-time myoelectric control for an anthropomorphic robotic hand was done as well.