Wearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

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Bimanual telemanipulation is vital for facilitating robots to complete complex and dexterous tasks that involve two handheld objects under teleoperation scenarios. However, the bimanual configuration introduces higher complexity, dynamics, and uncertainty, especially in those uncontrolled and unstructured environments, which require more advanced system integration. This paper presents a bimanual robotic teleoperation architecture with modular anthropomorphic hybrid grippers for the purpose of improving the telemanipulation capability under unstructured environments. Generally, there are two teleoperated subsystems within this architecture. The first one is the Leap Motion Controller and the anthropomorphic hybrid robotic grippers. Two 3D printed anthropomorphic hybrid robotic grippers with modular joints and soft layer augmentations are designed, fabricated, and equipped for telemanipulation tasks. A Leap Motion Controller is used to track the motion of two human hands, while each hand is utilized to teleoperate one robotic gripper. The second one is the haptic devices and the robotic arms. Two haptic devices are adopted as the master devices while each of them takes responsibility for one arm control. Based on such a framework, an average RMSE (root-mean-square-error) value of 0.0204 rad is obtained in joint tracking. Nine sign-language demonstrations and twelve object grasping tasks were conducted with the robotic gripper teleoperation. A challenging bimanual manipulation task for an object with 5.2 kg was well addressed using the integrated teleoperation system. Experimental results show that the proposed bimanual teleoperation system can effectively handle typical manipulation tasks, with excellent adaptabilities for a wide range of shapes, sizes, and weights, as well as grasping modes.

The motion control of high-precision electromechanitcal systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

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