The incorporation of human-like gripping capabilities enhances the flexibility and adaptability of robotic hands (RH) within dynamic production environments, serving as a critical factor in advancing industrial automation and intelligence. To facilitate cost reduction in the RH industry without compromising performance, humanoid gripping capabilities have gained popularity and become more economically accessible. This paper presents the design of a cost-effective underactuated adaptive RH (UARH), characterized by a link-spring telescopic rod-contact plate-slide mechanism. Upon actuation of the metacarpophalangeal joint, the proximal interphalangeal and distal interphalangeal joints achieve the desired grasping configuration through the preload state of the spring telescopic rod or the geometric characteristics of the object being grasped. Static modeling analysis reveals effective interaction and linkage relationships among the driving force, contact force, joint angle, and contact position of this UARH. The results from prototype testing confirm these characteristics. Consequently, it is evident that this UARH exhibits excellent dexterity, adaptability, and grasping capability, while maintaining a simple structure and low cost.

Snake-like robots face critical challenges in energy-efficient locomotion and smooth gait transitions, limiting their real-world deployment. This study introduces a bio-inspired compliant joint design integrated with a hierarchical neural oscillator network and an energy-optimized control framework. The joint mimics biological skeletal flexibility using specialized wheeled mechanisms and adaptive parallel linkages, while the control network enables adaptive gait generation and seamless transitions through a phase-smoothing algorithm. Critically, this work adopts a synergistic design philosophy where mechanical components and control parameters are co-optimized through shared dynamic modeling. The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. Experimental simulations demonstrate that the method achieves an 18% higher average forward speed (0.0563 m/s vs. 0.0478 m/s) with 7% lower energy use (0.1952 J vs. 0.2107 J) compared to conventional approaches. Physical prototype testing confirms these improvements under real-world conditions, showing a 12.9% speed increase (0.0531 m/s vs. 0.0470 m/s) and 7.3% energy reduction (0.2147 J vs. 0.2317 J). By unifying mechanical flexibility and adaptive control parameter tuning, this work bridges dynamic performance and energy efficiency, offering a robust solution for unstructured environments.

Rigid–flexible coupled robots hold significant potential for operating in unstructured environments, but a systematic analysis of their actuation strategies across diverse physical fields is notably lacking in the literature. This review addresses this gap by introducing a novel taxonomy based on field-controlled evolutionary pathways—mechanical → electromagnetic → chemical → biohybrid—and critically examining over 100 seminal studies through a six-dimensional framework encompassing design, dynamics, and performance. We demonstrate that hybrid field integration (e.g., pneumatic-chemical synergy) improves grasping robustness by 40% in cluttered environments compared to single-field systems. Notably, biohybrid actuators, which integrate living cells, exhibit over 90% motion similarity to biological models, while phase-transition materials allow for adaptive stiffness tuning (0.1–5 N·mm⁻¹) in medical applications. Radar chart analysis further reveals fundamental trade-offs between energy efficiency, response speed, and scalability across the various fields. These insights provide a clear roadmap for the development of next-generation robots with embodied intelligence, emphasizing multi-field synergies and bio-inspired adaptability.