The core of cross-domain intelligence lies in how different domains collaborate through synergistic mechanisms to achieve functions unattainable by a single domain.
The fusion of physical and biological intelligence combines engineered structures with natural actuation, compensating for the limitations of individual domains. Bioinspired structural PI designs physical structures by mimicking biological geometries (such as the helical tail of bacteria), enabling robots to move efficiently in low-Reynolds-number fluids. Bioinspired functional PI materials themselves mimic biological sensitivity to the environment, for example, using stimuli-responsive hydrogels to automatically deform or release drugs in specific biochemical environments (such as acidic tumor microenvironments).
In hybrid biological–physical systems, cells or microorganisms are combined with synthetic materials, where the biological component provides natural mobility and chemotaxis, while the synthetic component provides external controllability, enabling complex applications such as tumor treatment and antimicrobial therapy.
The combination of physical intelligence and computational intelligence integrates physical entities with data-driven algorithms to address control problems in complex environments. Its typical architecture is field–embodied feedback: CI algorithms are responsible for global perception and planning, controlling external fields (magnetic fields, acoustic fields, etc.), while PI physical structures are responsible for converting these fields into specific motion or deformation. The system connects the two through feedback loops to achieve hierarchical control.
Bidirectional coupling: 1. Using machine learning algorithms to assist in designing the morphology and controllers of microrobots, reducing trial-and-error costs; 2. Using physical prior knowledge to optimize perception and control algorithms, assisting neural networks in depth estimation under data scarcity.
The combination of biological intelligence's adaptability and chemical communication with computational intelligence's perception and control algorithms focuses on functional complementarity at the perception and control levels. 1. Using deep learning algorithms to address the difficulty of identifying and tracking biohybrid robots in complex backgrounds; 2. Drawing inspiration from biological natural behaviors (such as chemotaxis) to train control strategies using reinforcement learning.
The collaboration between computational intelligence and human experts can improve automation levels while ensuring safety. Through shared control frameworks, algorithms are responsible for low-level attitude stabilization and path planning, while humans are responsible for high-level strategic decisions. Augmented reality (AR/VR) interfaces and haptic feedback provide immersive operational experiences, allowing experts to intervene promptly to correct anomalies.
PI–CI–HI Synergy: A typical scenario is image-guided minimally invasive surgery. Physical robots (PI) serve as execution platforms, CI provides real-time localization and augmented display, and doctors (HI) perform supervision and decision-making.
PI–BI–CI Synergy: A typical scenario is targeted delivery of stem cell populations. PI provides macroscale transport capability, BI exerts biological therapeutic effects after arrival, and CI is responsible for precise navigation and imaging monitoring throughout the process.