Shuang Cong

The replication of human hand function has always been a crucial focus in the field of cooperative robots. Among various driving mechanisms, cable-driven dexterous hands have gained great attention for their ability to effectively mimic the dynamic characteristics of the human hand. However, achieving comparable degrees of freedom (DOFs), fingertip force, and manipulability to those of the human hand remains a challenge. In this study, we present a novel cable-driven dexterous hand featuring gear rolling joints and tension amplification mechanisms. Our dexterous hand has 16 DOFs (20 joints), a weight of 1.2 kg, a fingertip force of 26 N, 18 contact force sensors, and 15 position sensors. Notably, we develop an arm-hand integrated system based on cable-driven mechanisms to facilitate seamless integration between the dexterous hand and robotic arm components. Finally, the experimental results demonstrate that our dexterous hand exhibits high load capacity, flexibility, and manipulability while completing various daily life tasks.

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions.

Dexterous manipulation in unstructured environments poses significant challenges due to the limitations of traditional robotic control methods, which often rely on precise kinematic and dynamic models with predefined behaviors, impeding adaptability and flexibility. This article presents a hierarchical control framework based on multimodal perception that integrates visual and force feedback for robotic variable impedance control. Unlike conventional approaches, the proposed framework incorporates a novel visual-feature-driven reward mechanism and dynamically learned deep kinematic and dynamic models. The high-level planner integrates visual and haptic information to optimize task execution, while the low-level controller employs a variable impedance strategy to adapt stiffness and compliance in response to task demands. Experimental validation is conducted on both simulated and real-world platforms, involving complex assembly tasks such as peg-in-hole insertion and plug-socket connections. Results demonstrate that the proposed approach significantly improves task success rates, adaptability, and robustness compared to existing methods. This article provides a comprehensive evaluation of visual-haptic integration for enhancing efficiency and generalization in contact-rich robotic manipulation tasks.