For cross-domain intelligent microrobots to reach clinical application, they need to address challenges in hardware and integration, control and coordination, autonomy and safety, and evaluation and benchmarking.
Multifunctional integration remains a major bottleneck for microrobots: simultaneously integrating responsive materials, magnetic/electronic components, and biological elements at the microscale is complex. Material compatibility is limited—stimuli may damage cells, while magnetic or electronic modules pose biological toxicity; manufacturing processes often rely on manual assembly, making scaling difficult.
Future needs include developing novel biocompatible materials, unified micro/nanofabrication processes, and promoting modular, reproducible designs.
Smart materials and biohybrid robots exhibit highly nonlinear behavior, and biological components have randomness, making traditional control algorithms inadequate. Methods such as reinforcement learning require large amounts of data and trial-and-error, which are unsuitable for in vivo scenarios; simulations have not yet accurately described fluid, chemical, and biological processes; swarm control is affected by visual and noise interference.
Solutions include developing physically constrained learning algorithms, reliable digital twin environments, and hybrid swarm communication strategies to achieve strategy transfer from in vitro to in vivo.
Enhancing autonomy must be built on the premise of safety and controllability. Highly autonomous microrobots perform medical procedures and require strict regulation; decisions are influenced not only by algorithms but also by material and biological safety constraints. Therefore, human-robot collaborative control, explainable decision-making, and safety certification compliant with medical regulations are needed.
Focus on shared control, verifiable safety strategies, and ethical standards to make autonomous systems acceptable in clinical settings.
Currently, there is a lack of unified standards and testing platforms, and different experimental conditions make results difficult to compare, hindering design optimization and regulatory approval. Cross-domain evaluation frameworks are needed, combining digital twins and standardized experiments to quantify performance and safety boundaries.
Establish public datasets, standard tasks, and evaluation metrics to promote transparent and fair technical validation.