This study presents an innovative hand exoskeleton based on a linkage-transmission mechanism, aimed at supporting individuals with motor impairments in executing grasping activities essential for daily life. An underactuated mechanism leveraging redundant degrees of freedom is proposed, enabling the exoskeleton to adaptively grasp objects of varying shapes and sizes. The mechanical design ensures kinematic compatibility between the finger joints and the exoskeleton by establishing appropriate constrained kinematic chains. Its kinematics and quasi-statics are systematically analyzed to optimize the mechanical design. To evaluate the functional performance of the assistive hand exoskeleton, a systematic assessment framework is developed, incorporating three dimensions: subjective perception, kinesiology, and physiological response. Two experimental paradigms were conducted using the hand exoskeleton prototype with six healthy participants. Six assessment metrics (motion transparency, grip similarity, muscle activation, muscle fatigue, the System Usability Scale (SUS), and the Rating of Perceived Exertion (RPE) scale) were employed to assess the exoskeleton’s functionality. Experimental results demonstrate that the proposed exoskeleton effectively aids users in grasping various objects while significantly reducing physical exertion and fatigue.

Current hand exoskeleton interaction methods primarily focus on recognizing a limited range of hand motion intentions and rely on pre-programmed control to execute predefined commands. However, these approaches face significant limitations when confronted with unanticipated or non-predefined scenarios, such as performing various gestures or grasping different objects. To address this challenge, this paper proposes an embodied interaction control (EIC) framework for hand exoskeletons based on a multimodal large language model (MLLM). First, an embodied interaction method leveraging multi-modal fusion of speech and image information is developed, enabling more intuitive, hands-free, accurate, and robust human-robot interaction. By utilizing multi-modal data, the MLLM infers the user’s hand motion intentions and generates corresponding motion plans for the exoskeleton. The underlying control strategy is then used to execute the motion planning. Notably, leveraging the advanced reasoning and code-generation capabilities of MLLMs, the framework can generate undefined gestures and grasping actions. Finally, experimental results validate the effectiveness and generalizability of the EIC framework.

Point-to-point and periodic motions are ubiquitous in the world of robotics. To master these motions, Autonomous Dynamic System (ADS) based algorithms are fundamental in the domain of Learning from Demonstration (LfD). However, these algorithms face the significant challenge of balancing precision in learning with the maintenance of system stability. This paper addresses this challenge by presenting a novel ADS algorithm that leverages neural network technology. The proposed algorithm is designed to distill essential knowledge from demonstration data, ensuring stability during the learning of both point-to-point and periodic motions. For point-to-point motions, a neural Lyapunov function is proposed to align with the provided demonstrations. In the case of periodic motions, the neural Lyapunov function is used with the transversal contraction to ensure that all generated motions converge to a stable limit cycle. The model utilizes a streamlined neural network architecture, adept at achieving dual objectives: optimizing learning accuracy while maintaining global stability. To thoroughly assess the efficacy of the proposed algorithm, rigorous evaluations are conducted using the LASA dataset and a massage robot task. The assessments were complemented by empirical validation, providing evidence of the algorithm’s performance.

Human-machine interaction technologies are crucial for enhancing human capabilities in the digital world. The hand, as a primary interaction tool, conveys information through gestures and tactile signals. With advancements in flexible electronics and fuzzy systems, it is now possible to achieve interpretable and accurate interactions using hand gestures and tactile information. This paper introduces a fuzzy-encoded, dual-modal soft glove applied to gesture and grasping object classification. The glove, featuring 10 soft pressure sensors and 6 soft bending sensors, is compactly designed and exhibits excellent sensitivity, with average sensitivities of 133 N$^{-1}$for soft pressure sensors and 13deg$^{-1}$for soft bending sensors. The Fuzzy Mamba Encoding (FuME) classification algorithm, inspired by the interpretability and uncertainty resilience of fuzzy systems and the fitting ability of state-space models, was developed. To the best of our knowledge, this work is the first to combine the Mamba structure with fuzzy intelligent systems. Applied to gesture and grasping object classifications, the glove achieved average accuracies of 96.3% and 94.9%, respectively. The fuzzy encoding leverages membership vectors provided by experts to enhance interpretability. These results demonstrate the glove's effectiveness in signal recording and processing and highlight the powerful synergy between flexible electronic technologies and fuzzy systems.

Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critical insights from demonstration data but also demonstrates the capacity to learn a candidate Lyapunov energy function that is consistent with the provided demonstrations. The model presented in this paper employs a simplistic neural network architecture that excels in fulfilling a dual objective: optimizing accuracy while simultaneously preserving global stability. To comprehensively evaluate the effectiveness of the proposed algorithm, rigorous assessments are conducted using the LASA dataset, further reinforced by empirical validation through a robotic experiment.

In manufacturing, musculoskeletal robots have gained more attention with the potential advantages of flexibility, robustness, and adaptability over conventional serial-link rigid robots. Focusing on the fundamental lifting tasks, a hybrid controller is proposed to overcome control challenges of such robots for widely applications in industry. The metaverse technology offers an available simulated-reality-based platform to verify the proposed method. The hybrid controller contains two main parts. A muscle-synergy-based radial basis function (RBF) network is proposed as the feedforward controller, which is able to characterize the phasic and the tonic muscle synergies simultaneously. The adaptive dynamic programming (ADP) is applied as the feedback controller to address the optimal control problem. The actor-critic structure is applied in the ADP-based controller, where the critic network is trained to approximate the optimal performance index and the actor network is trained to compute the optimal muscle excitations. Furthermore, the convergence and stability of the ADP algorithm are also analyzed. Finally, experiments have been designed to verify the effectiveness of this hybrid controller on an upper limb musculoskeletal system, and the comparisons with other controllers are also illustrated. The results show that the proposed controller can obtain a satisfactory performance for lifting tasks.