With the rapid development of robotics and artificial intelligence, people's vision of "humanoid robots entering daily life" is no longer just a figment of science fiction. When a monitoring robot discovers spilled coffee in a corridor, it can directly "call" a nearby cleaning robot to handle it; when a delivery robot runs out of power, it can "hand over" the package to another robot along the way to continue the task; when an elderly person falls at home, a companion robot can instantly coordinate with community patrol robots and medical AI to form a rescue team.
However, to realize this vision, the interconnected and collaborative infrastructure between robots remains a significant gap to be filled. Currently, most robot systems are isolated from each other, lacking a unified language and standards across different manufacturers, hindering the sharing of experience and intelligence. This makes it difficult for "single-machine intelligence" to fully function in complex real-world environments. The OpenMind project targets this gap in robot collaboration—by creating the open robot operating system OM1 and the decentralized collaboration network FABRIC, it aims to establish a common "nervous system" for intelligent machines worldwide. In other words, OpenMind is committed to transforming individual robots from closed "intelligence islands" into collaborative entities connected in an open network, enabling robots from different brands to securely and reliably access this global network, share knowledge, and collaboratively perform tasks.
This research report will provide an in-depth analysis of the OpenMind project's industry background, technical architecture, ecosystem model, competitive landscape, risk assessment, and future prospects, dissecting this cutting-edge exploration at the intersection of AI, blockchain , and robotics.
Author: 0xstride , Researcher at Web3Caff Research
Cover: Logo from OpenMind, Photo by Andrew Kliatskyi on Unsplash , Typography by Web3Caff Research
Word count: 17,600+ words in total
Table of contents
- Industry Background Analysis: Market Gaps and Technological Evolution Path of Robot Collaborative Infrastructure
- In-depth analysis of the technical architecture: the systematic design of OM1, Fabric, and on-chain primitives
- I. OM1 Universal Software Foundation: Unifying sensors, models, and actions into a "textual world capable of reasoning"
- Understanding the basics of robot collaboration
- From perception to semantics: Enabling robots to "understand" the world
- From Semantics to Decision-Making: Reasoning Within Clear Boundaries
- From decision-making to execution: Controllable implementation
- Making complex systems operable
- II. FABRIC Collaboration Layer and On-Chain Primitives: Upgrading "Collaboration" from a Platform Capability to a Verifiable Network Capability
- I. OM1 Universal Software Foundation: Unifying sensors, models, and actions into a "textual world capable of reasoning"
- Ecological Model Closed Loop: From Value Creation to the Self-Reinforcing Flywheel Effect
- The three-tiered operational mechanism of the ecological closed loop
- First layer: Foundation of identity and trust
- Second layer: Task allocation and collaborative execution
- Third layer: Verifiable settlement and incentive allocation
- End-to-end scenario perspective: the complete value transfer chain
- Scenario 1: Self-organized collaboration in smart building services
- Scenario 2: Large-scale collaboration and cross-regional interoperability of urban delivery networks
- Scenario 3: Privacy Protection and Emergency Response in Home Healthcare Scenarios
- The three-tiered operational mechanism of the ecological closed loop
- Competitive Landscape Overview: OpenMind's Strategic Positioning at the Crossroads of DePIN and the Robotics Industry
- Risk Matrix Assessment: Systemic Risk Analysis Across Technology, Ecosystem, and Compliance Dimensions
- Technical aspects: Engineering constraints on LLM nondeterminism and physical world security
- Ecological Level: The Game Equilibrium of Cold Start in Two-Sided Markets and Incentive Mechanisms
- Compliance aspects: Attribution of responsibility, privacy protection, and the legal boundaries of tokens
- Future Outlook: From Vision Narrative to Verifiable Milestones
- In-product optimization: Task & skill combo market
- External Product Optimization: From "Robot Applications" to "Robot Society"
- Key Points Structure Diagram
- References
Industry Background Analysis: Market Gaps and Technological Evolution Path of Robot Collaborative Infrastructure
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 is like an information silo, unable to flow knowledge or reuse experience, 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 with each other, 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 movements 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 beginning to be considered one of the important development directions for the next generation of 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 proprietary protocols alone cannot solve the problems of identity trust and incentives in open and heterogeneous environments, which provides an opportunity to introduce Web3 technologies such as blockchain. This adaptability stems from the structural fit between the essential needs of open and heterogeneous robot networks—identity mutual recognition, data trustworthiness, value transfer, and behavioral constraints—and the characteristics of blockchain technology. From existing practices, decentralized identity ( DID ) technology has been explored and applied in machine networks: peaq ID and IoT eX's ioID provide devices with an autonomous identity framework, while the RoboComm project achieves scalable robot-to-robot encrypted communication through state channels. Regarding data trustworthiness, the DePIN field has accumulated mature experience—Helium prevents cheating through multi- node mutual verification, and 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 robot scenarios, and OpenMind, based on the above technological explorations, is attempting to integrate identity, data, incentives, and collaboration into a complete protocol stack for robot scenarios.
In-depth analysis of the technical architecture: the systematic design of OM1, Fabric, and on-chain primitives
Founded in San Francisco in 2024 by Jan Liphardt, a professor of biophysics at Stanford University, OpenMind's core belief is that the collaboration challenges facing the robotics industry are fundamentally similar to those in biological systems: "If AI is the brain and robots are the body, then the coordination mechanism is the nervous system." Based on this understanding, OpenMind positions itself as "the Android of robotics," attempting to solve the industry's fragmentation problem through an open-source , hardware-independent software layer. In August 2025, the company completed a $ 20 million funding round led by Pantera Capital, with participation from Coinbase Ventures, Pi Network Ventures, and others.
In terms of specific technical approaches, OpenMind's design logic revolves around three core issues [1].
When robots truly enter the real world, the real challenge is often not "getting them moving." The deeper challenges lie in: how can robots maintain stable perception and decision-making in noisy environments? How can trust be established between multiple robots when they need to collaborate? Who defines the boundaries of robot behavior, and how is payment made after the work is completed?
These three types of problems respectively point to the reliability of individual intelligence, the trust foundation for group collaboration, and the closed-loop capability of machines participating in economic activities. OpenMind's approach is not to stack "stronger models" at a single stage, but rather to systematize and interface these problems using a three-layer architecture:
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