Biosyncretic robots, which are new nature-based robots in addition to bionic robots, that utilize biological materials to realize their core function, have been supposed to further promote the progress in robotics. Actuation as the main operation mechanism relates to the robotic overall performance. Therefore, biosyncretic robots actuated by living biological actuators have attracted increasing attention. However, innovative propelling modes and control methods are still necessary for the further development of controllable motion performance of biosyncretic robots. In this work, a muscle tissue-based biosyncretic swimmer with a manta ray-inspired propelling mode has been developed. What is more, to improve the stable controllability of the biosyncretic swimmer, a dynamic control method based on circularly distributed multiple electrodes (CDME) has been proposed. In this method, the direction of the electric field generated by the CDME could be real-time controlled to be parallel with the actuation tissue of the dynamic swimmer. Therefore, the instability of the tissue actuation induced by the dynamic included angle between the tissue axis and electric field direction could be eliminated. Finally, the biosyncretic robot has demonstrated stable, controllable, and effective swimming, by adjusting the electric stimulation pulse direction, amplitude, and frequency. This work may be beneficial for not only the development of biosyncretic robots but also other related studies including bionic design of soft robots and muscle tissue engineering.

Assist-as-needed (AAN) assistance can promote active voluntary participation in rehabilitation and motor function recovery of post-stroke patients. However, different patients have personalized damaged regions and recovery states, causing difficulties to obtain adaptive and customized assistance in robot-assisted rehabilitation. This paper presents a mirror Adaptive Assist-As-Needed (AAAN) scheme, including two modules of Multi-Sensors Fused Estimation (MSFE) and Online Incremental Mirror Adaptation (OIMA) , to encourage the subjects to actively participate in rehabilitation. Specifically, the first MSFE module can obtain the needed assistance based on the functional capability of the post-stroke patients via the data fusion of biological and motional signals using Kalman Filter. The second OIMA module fine-tunes the control torques estimated by MSFE to adapt the muscle fatigue and stiffness varieties of the affected limb based on the motion and physiological reference of the mirror healthy limb. The results demonstrate that the AAAN strategy can realize the transparent mode for healthy subjects and promote post-stroke patients to rehabilitate the affected limb with active participation using EMG signals 90.5% similar to those of the mirror healthy limb. The proposed method can be expected to greatly enhance power assistance and rehabilitation outcome of post-stroke patients using exoskeletons by provoking active participation. Note to Practitioners —For robotic rehabilitation, it is crucial to provide suitable assistances that can maximize the participation of post-stroke patients, which can promote the recovery outcome of therapies. The main purpose of this work is to achieve the adaptive assist-as-needed control strategy for upper limb rehabilitation tasks in two steps. Firstly, the elbow joint torques of a post-stroke patient are estimated by data fusion of motion and electromyography (EMG) signals using Kalman Filter, which can make up for the shortcomings of the individual signals, such as poor reliability and low sensitivity. Secondly, the joint motion and EMG signals of the mirror healthy limb are used as the reference to calculate the adaptive needed assistance to rehabilitate the affected limb. The preliminary experiments with healthy and post-stroke subjects demonstrate that this approach can obtain stable motion with the natural physiological states of subjects and enhance active voluntary participation in rehabilitation. In the future study, it will be investigated how to accelerate the adaptation of new patients based on the knowledge of the learned individuals using machine learning methods, such as lifelong learning and incremental learning.

Soft actuators have received extensive attention in the fields of soft robotics, biomedicine, and intelligence systems owing to their advantages of pliancy, silence, and essential safety. However, most existing soft actuators have only single actuation elements and lack sensing. Therefore, it is difficult for them to perform complex motions with multiple degrees of freedom (multi-DOFs) and high precision. This article reports a miniature columnar dielectric elastomer actuator (DEA) with multi-DOF actuation and sensing, which was fabricated with an electroactive polymer acrylic film (Very High Bond [VHB] acrylic film by 3M Company) and carbon black grease electrodes. The arrangement of the simulation electrodes on the VHB was optimized to realize multi-DOF actuation, and the sensing electrodes were configured on the outer part of the DEA to realize real-time sensing. The results showed that the soft actuator can achieve all-round actuation through the selective power of the stimulation electrodes with a controllable voltage. The maximum bending angle and axial strain of the actuator reached 50° and 13%, respectively. Moreover, the deformation modes, direction, and quantity could be precisely measured using the integrative sensing function. In addition, to demonstrate the advantages of the proposed actuator, a manipulator with multiple actuators was designed and controlled to realize different actions of screwing and grasping with sensing. This research is useful not only for the design of multifunctional soft actuators but also for the development of soft robots with flexible, complex, and precisely controllable motions.

This review has highlighted the WR properties of different materials and their related actuators, which may exhibit remarkable performance in various fields, such as artificial muscles, smart sensors, robots, and new energy generators.

Fish can adaptively adjust their body kinematics and swimming modes by sensing to realize optimal propulsion. However, most soft robotic fish have an unchangeable swimming mode through simple structure design, making them difficult to adapt to dynamic and complex fluid environments. Here, inspired by the multiple muscle synergy and lateral line sensing function of fish, we developed a soft robotic fish with multiple actuating units and embedded sensing elements. By collaboratively controlling the amplitude and phase of excitation from the multiple flexible actuating units, the soft robotic fish can successfully realize various swimming modes very similar to those of natural fish. Additionally, the embedded flexible sensing elements enable the robotic fish to sense the swimming state and the surrounding fluid environment in real time. The multiple actuation and embedded sensing allow the soft robotic fish to adaptively switch to an optimal swimming mode in a certain fluid environment. The multi-mode swimming and perception capabilities proposed in this work not only make soft robotic fish more intelligent and adaptable to complex fluid environments, but also contribute to the future implementation of autonomous control capabilities for robotic fish.