In the traditional paradigm, robots are typically designed and deployed as standalone systems, operating independently. This model suffers from significant structural constraints: robots from different manufacturers often run their own closed hardware and software stacks, incompatible with each other and struggling to communicate and collaborate. This means each robot acts as an information silo, hindering knowledge flow and experience reuse, resulting in a huge waste of R&D investment and limited functionality. For example, delivery robots from different brands on the same street may not coordinate, unable to share road information or avoidance strategies; similarly, two warehouse robots from unfamiliar manufacturers, without a unified protocol, may compete for the same goods or have route conflicts. No matter how powerful standalone intelligence is, its limitations become apparent in a multi-robot environment: without coordination, there is no true intelligence; only individual movement without global optimization. As robots move from factories into more open public spaces and human society, the need for collaboration is becoming more urgent than ever. A collaborative layer architecture that enables different robots to "speak a common language," understand each other's intentions, and make collaborative decisions is increasingly seen as a key direction for the development of next-generation robots. This has spurred the evolution of technologies related to robot swarm intelligence and multi-agent systems: from centralized multi-machine scheduling systems to distributed swarm collaboration algorithms, and then to "robot swarm intelligence" achieved using machine learning and communication networks. However, traditional internet and private protocols alone cannot solve the problems of identity trust and incentives in open, heterogeneous environments. This provides an opportunity to introduce Web3 technologies such as blockchain. This adaptability stems from the structural fit between the essential needs of open, heterogeneous robot networks—identity mutual recognition, data trustworthiness, value transfer, and behavioral constraints—and the characteristics of blockchain technology. In existing practices, decentralized identity (DID) technology has been explored and applied in machine networks: peaq ID and IoTeX's ioID provide autonomous identity frameworks for devices, while the RoboComm project has achieved scalable robot-to-robot encrypted communication through state channels. In terms of data trustworthiness, the DePIN field has accumulated mature experience—Helium prevents cheating through multi-node mutual authentication, while Hivemapper and NATIX ensure the quality of crowdsourced map data through encrypted verification. These mechanisms, combined with DeFi, provide a referable technical roadmap for multi-party participation in robotics scenarios. OpenMind, building upon these technological explorations, is attempting to integrate identity, data, incentives, and collaboration into a complete protocol stack for robotics scenarios. The above content is excerpted from Web3Caff Research's "Machine Economy On-Chain Infrastructure: OpenMind 17,000-Word Research Report: In the Robot Era, Will Web3's Value Fully Explode? A Panoramic Analysis of its Technical Architecture, Ecosystem Model, Competitive Landscape, and Future Prospects" Click to view the full version 👇