Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although advancements in robotic autonomy, human operation remains essential in complex aerial environments. Existing teleoperation approaches for multirotors fail to fully leverage the additional DoFs provided by omnidirectional rotation. Additionally, the dexterity of human fingers should be exploited for more engaged interaction. In this work, we propose an aerial teleoperation system that brings the rotational flexibility of human hands into the unbounded aerial workspace. Our system includes two motion-tracking marker sets—one on the shoulder and one on the hand—along with a data glove to capture hand gestures. Using these inputs, we design four interaction modes for different tasks, including Spherical Mode and Cartesian Mode for long-range moving, Operation Mode for precise manipulation, as well as Locking Mode for temporary pauses, where the hand gestures are utilized for seamless mode switching. We evaluate our system on a vertically mounted valve-turning task in the real world, demonstrating how each mode contributes to effective aerial manipulation. This interaction framework bridges human dexterity with aerial robotics, paving the way for enhanced aerial teleoperation in unstructured environments.

The vague interpretation of myoelectrical signals on the residual limb end makes restoring dexterous hand function in amputees still impossible. Understanding motor control between human motion intention and synaptic inputs to motor neurons also remains a significant challenge. The neural decoding methods of surface EMG signals remains challenging, which limit the application of robot hand in real life. Herein, we propose and substantiate a human-machine interface for motor control that introduces neural information of motor neurons in conjunction with the combination mechanism of muscle contraction. The interface firstly introduces a new concept of motor unit (MU) spike trains, which combines decoupling of the electrical activations on motor neuron axons with extraction of motion patterns from the discharge timings of the motor neuron pools. We realized a real-time implementation of the EMG decomposition algorithm on our developed prosthesis hand control system. The control scheme provides an accurate classification of intuitive hand motions, enabling the amputee to perform versatile finger movements of the prosthesis hand. The concept of motor neuron discharge timings was evaluated through experiments on one amputee participant and six able-bodied participants. The results show that the neuroprosthesis hand control scheme based on MU spike trains has the capacity of generating accurate and intuitive hand movements for amputees in a physical environment.

Gesture classification and motion speed regression are always two major issues in myoelectrical prosthetic hand research. However, there is little research considering these two issues in conjunction. Some shared EMG feature information in these two processing tasks is promising to improve the performance of prosthetic hand control. In this study, a joint-loss (JL) neural network architecture is proposed to implement gesture classification and motion speed regression problems in parallel by sharing the hidden neural units in the training process and optimizing the joint loss function. We evaluated the proposed control system through motion experiments performed on six participants. The experiment result shows that the classification and regression models can successfully reproduce smooth movement based on EMG measurement with high accuracy. Furthermore, the possibility of clinical application is demonstrated through the online movement of the real prosthetic hand.