Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems.

Marine invertebrates exhibit diverse thermoregulatory capabilities enabled by hierarchical architectures, porous skeletal frameworks, and adaptive interfaces. These biological features provide engineering cues for controlling heat conduction, convection, and radiation, particularly when lightweight and multifunctional thermal designs are required. This review surveys marine-invertebrate-inspired thermal management from an engineering perspective and synthesizes biological structure–function relationships into transferable design concepts. Literature was collected from Scopus, Web of Science, and Google Scholar. Studies were included if they (i) explicitly referenced marine invertebrate morphology, structural organization, interfacial behavior, or adaptive mechanisms and (ii) quantitatively reported thermal metrics such as thermal conductivity, heat capacity/latent heat, heat dissipation performance, or temperature modulation. To maintain biological scope while enabling cross-comparison, the review is organized across major marine invertebrate phyla frequently used in bioinspired engineering—Mollusca, Porifera, Cnidaria, Echinodermata, and Arthropoda—and the engineering literature is classified into three categories: (A) bio-inspired functional materials for thermal transport or optical–thermal control; (B) bio-inspired structural architectures that guide heat flow via hierarchical or porous geometries; and (C) integrated thermal management systems that couple multiple mechanisms at the device or system scale. Across these categories, the reviewed studies demonstrate promising routes toward electronics cooling and aerospace thermal protection. Remaining challenges include scalable fabrication over large areas, flow uniformity in microchannel-based platforms, and long-term reliability under combined pressure, salinity, and thermal cycling.

While walking robots possess significantpotential for various real-world applications, the reliance on high-performance sensors and complex control architectures for precise gait control remains a significant barrier to commercialization and lightweight design. To overcome these engineering limitations and lay the groundwork for a sensing paradigm adaptable to complex terrains, this study proposes an AI-based sensorless feedback control framework that incorporates the biological principles of proprioception. To this end, a walking robot leveraging the morphological intelligence of the Klann linkage was developed. We constructed a time-series dataset by defining motor current signals as ‘interoceptive sensing’ information—analogous to biological muscle feedback—and synchronizing them with absolute angular data. This dataset was used to train an Attention-LSTM (A-LSTM) model, which predicts future motor states in real-time by decoding nonlinear physical information embedded within internal current data, independent of external environmental sensors. By integrating the proposed model into a PI controller, a stable biomimetic walking loop was successfully implemented without the need for additional position sensors.

Wide-bandgap (WBG) power converters impose stringent requirements, including high-frequency switching, strongly nonlinear dynamics, and limited computational time, which constrain conventional control and artificial intelligence (AI)-based approaches and hinder their practical deployment. Existing review studies have primarily focused on algorithmic structures or performance, while systematic analyses from a deployment feasibility perspective under hardware constraints remain limited. This paper examines AI applications in WBG power converters from a system-level deployment perspective and analyzes existing studies based on implementation feasibility. After outlining the physical characteristics and control requirements of WBG devices, it reviews AI-based modeling, AI-assisted model predictive control (MPC), and reinforcement learning (RL)-based direct control. These approaches are evaluated in terms of computational complexity, real-time feasibility, out-of-distribution (OOD) generalization, and integration with conventional control frameworks. Key deployment challenges, including safety-constrained RL, sim-to-real transfer, and field-programmable gate array (FPGA)/embedded implementation, are treated as core analytical dimensions. To support this assessment, this review introduces an AI Deployment Readiness framework organized around four analytical dimensions: (1) modeling accuracy, (2) safety assurance, (3) sim-to-real transfer capability, and (4) hardware implementability. Using this framework, prior studies are reassessed, and its applicability is further discussed for applications such as fault diagnosis and remaining useful life (RUL) prediction. The analysis identifies key bottlenecks and clarifies deployment-relevant considerations for high-frequency WBG systems.