Dexterous prosthetic hand is an essential rehabilitation assistant device to improve the life quality of amputee patients. Despite the continuous emergence of commercial prostheses and laboratory prototypes, the rejection rate remains high caused by the poor neural interaction performance and excessive cognitive burden, especially for the usage of upper-arm prostheses controlled by above-the-elbow amputation stump. The progress in artificial perception, bidirectional neural interface and share control indicates a great potential to improve the manipulation efficiency of upper-limb prostheses. In this review, a comprehensive analysis of human-in-the-loop shared control studies is presented to provide researchers with a systematic technical route in upper-limb prostheses control. The latest avenues of research concerning myoelectric control, sensory feedback, perception, autonomous motion planning of multiple degree of freedom elbow-wrist, and share control are overviewed and discussed. The comprehensive assessments show that there remains inadequate technologies to achieve an anthropomorphic and efficient unified elbow-wrist-hand prostheses manipulation. By delineating the current shortcomings, the outcomes of this work highlight future investigation in the field of intuitive motion control, feedback of proprioception/touch and the natural interaction between human intent and machine autonomy.

This letter introduces a variable admittance control approach aimed at enhancing intuitive human-robot interaction by considering both direct and indirect human intentions. The magnitude of force serves as a representation of direct intentions, delineating preferences for rapid or precise motions. Drawing from the movement sciences and motor control field, the minimum-jerk model is employed to mirror human motor system control policies and movement behaviors. From this model, velocity-curvature patterns are derived, enabling an intuitive estimation of indirect intentions indicating long-term objectives like trajectory and turning direction. We propose an innovative guidance method, rooted in the estimation of indirect human intentions, enabling the robot to concurrently follow and guide the operator. To assess the efficacy of this approach, both offline simulations and real-time human experiments are conducted on a six-DOF robot and a force/torque sensor. Our comprehensive experiments demonstrate substantial enhancements in both accuracy ( ${\geq\!\text{12.1}{\%}}$ ) and smoothness ( ${\geq\!\text{18.3}{\%}}$ ) over fixed admittance control and state-of-the-art variable admittance methods.

Anthropomorphic robotic hands have been widely investigated to dexterously manipulate objects because of their anatomical similarity to the human hand. However, the large dimension of configuration space challenges the real-time performance of existing grasp planning methods and drastically limits the application of anthropomorphic hands. In this letter, we propose a fast force-closure grasp synthesis (FFCGS) method for the anthropomorphic hand to efficiently grasp unknown objects. The FFCGS is implemented by using a signed distance field (SDF) as input. Firstly, a network that samples feasible 6D wrist poses is trained in an end-to-end fashion to reduce the dimension of search space. Furthermore, a fast optimization algorithm is presented to find finger configurations for force-closure precision grasp based on the differentiable Q-distance metric. We validate our method in both a simulated and a real-world environment. Experiment results show that the proposed FFCGS achieves a significantly improved performance in terms of time efficiency (5 times faster), grasp quality metrics, and success rate (5%–10% improvement) over benchmark methods. The outcomes of this study have great significance in promoting the motion planning of robot hand-arm systems and upper-limb prostheses.

Grasping multiple affordance parts and from arbitrary directions for complex shaped objects still remains a challenging problem for prosthetic hand with wrist. We propose a semi-autonomous control method that uses only an integrated in-hand camera to predict the final grasping part on an object as the hand approaches it and obtain the appropriate wrist joint angles and preshape type. We collect approach-grasp motion sequences from human experts to construct a motion prior field (MPF) and derive the prediction model MPFNet by behavior cloning. With noise augmentation and a hybrid regression-categorization policy training, our prediction model gets less than 2 cm predicting deviation under a small number (15) of demonstrations for each object. We apply our control method to a prosthetic hand with a 2 $^{\circ }$ -of-freedom (DoF) wrist, enabling it to grasp multiple parts of complex shaped objects and remain robust under the position and orientation variation. Compared to state-of-the-art myoelectric control and semi-autonomous control methods, respectively, our method improves 65.4%/26.3% in grasp success rate, 40.4%/26.3% in control time, and 35.6%/27.8% in error distance. Furthermore, our method is adaptable to different objects in the same category.