Written by: Jacob Zhao @IOSG
Robot Panorama: From Industrial Automation to Humanoid Intelligence
The traditional robotics industry chain has formed a complete hierarchical system from the bottom up, covering four major links: core components—intermediate control systems—complete machine manufacturing—application integration. Core components (controllers, servos, reducers, sensors, batteries, etc.) have the highest technological barriers, determining the performance and cost limits of the complete machine; the control system acts as the "brain and cerebellum" of the robot, responsible for decision-making, planning, and motion control; complete machine manufacturing reflects the ability to integrate the supply chain. System integration and application determine the depth of commercialization, which is becoming the new core of value.
According to application scenarios and forms, global robotics is evolving along the path of "industrial automation → scene intelligence → general intelligence," forming five major types: industrial robots, mobile robots, service robots, special robots, and humanoid robots.
Industrial Robots
Currently the only fully mature track, widely used in manufacturing processes such as welding, assembly, painting, and handling. The industry has formed a standardized supply chain system, with stable profit margins and clear ROI. Among them, the subclass of collaborative robots (Cobots) emphasizes human-machine collaboration, lightweight deployment, and is growing the fastest.
Representative companies: ABB, Fanuc, Yaskawa, KUKA, Universal Robots, JAKA, AUBO.
Mobile Robots
Including AGV (Automated Guided Vehicles) and AMR (Autonomous Mobile Robots), which have been widely implemented in logistics warehousing, e-commerce delivery, and manufacturing transportation, becoming the most mature category for B-end applications.
Representative companies: Amazon Robotics, Geek+, Quicktron, Locus Robotics.
Service Robots
Targeting industries such as cleaning, dining, hospitality, and education, this is the fastest-growing area on the consumer side. Cleaning products have entered the realm of consumer electronics, while medical and commercial delivery are accelerating commercialization. Additionally, a number of more general-purpose operational robots are emerging (such as Dyna's dual-arm system)—more flexible than task-specific products, but not yet achieving the versatility of humanoid robots.
Representative companies: Ecovacs, Roborock, PuduTech, Qianlong Intelligent, iRobot, Dyna, etc.
Special Robots
Primarily serving scenarios in medical, military, construction, marine, and aerospace fields, the market size is limited but profit margins are high and barriers are strong, often relying on government and corporate orders, and are in a stage of vertical segmentation growth. Typical projects include intuitive surgery, Boston Dynamics, ANYbotics, NASA Valkyrie, etc.
Humanoid Robots
Regarded as the future "general labor platform."
Representative companies: Tesla (Optimus), Figure AI (Figure 01), Sanctuary AI (Phoenix), Agility Robotics (Digit), Apptronik (Apollo), 1X Robotics, Neura Robotics, Unitree, UBTECH, Zhiyuan Robotics, etc.
Humanoid robots are currently the most focused frontier direction, with their core value lying in their humanoid structure adapting to existing social spaces, seen as a key form towards the "general labor platform." Unlike industrial robots that pursue extreme efficiency, humanoid robots emphasize general adaptability and task transferability, capable of entering factories, homes, and public spaces without modifying the environment.
Currently, most humanoid robots remain in the technical demonstration stage, primarily validating dynamic balance, walking, and operational capabilities. Although some projects have begun small-scale deployment in highly controlled factory scenarios (such as Figure × BMW, Agility Digit), and more manufacturers (such as 1X) are expected to enter early distribution starting in 2026, these are still limited applications of "narrow scenarios and single tasks," rather than truly realizing general labor. Overall, it will take several more years to achieve large-scale commercialization. Core bottlenecks include: control challenges such as multi-degree-of-freedom coordination and real-time dynamic balance; energy consumption and endurance issues constrained by battery energy density and drive efficiency; instability and difficulty in generalizing perception-decision links in open environments; significant data gaps (difficult to support general strategy training); unresolved cross-form transfer; and hardware supply chain and cost curves (especially outside of China) still pose real barriers, further increasing the difficulty of achieving large-scale, low-cost deployment.
The future commercialization path is expected to go through three stages: short-term dominated by Demo-as-a-Service, relying on pilot projects and subsidies; mid-term evolving into Robotics-as-a-Service (RaaS), building a task and skill ecosystem; and long-term focusing on labor cloud and intelligent subscription services, shifting the value center from hardware manufacturing to software and service networks. Overall, humanoid robots are in a critical transition period from demonstration to self-learning, and whether they can overcome the triple barriers of control, cost, and algorithms will determine if they can truly achieve embodied intelligence.
AI × Robotics: The Dawn of the Era of Embodied Intelligence
Traditional automation mainly relies on pre-programming and assembly line control (such as the perception-planning-control DSOP architecture), which can only operate reliably in structured environments. The real world is more complex and variable, and the new generation of embodied intelligence (Embodied AI) follows a different paradigm: through large models and unified representation learning, enabling robots to possess cross-scenario "understanding-prediction-action" capabilities. Embodied intelligence emphasizes the dynamic coupling of body (hardware) + brain (model) + environment (interaction), where the robot is the carrier, and intelligence is the core.
Generative AI belongs to the intelligence of the language world, excelling at understanding symbols and semantics; embodied intelligence belongs to the intelligence of the real world, mastering perception and action. The two correspond to "brain" and "body," representing two parallel main lines of AI evolution. From an intelligence hierarchy perspective, embodied intelligence is a higher level than generative AI, but its maturity is still significantly lagging. LLMs rely on vast amounts of internet data to form a clear "data → computing power → deployment" closed loop; whereas robotic intelligence requires first-person, multi-modal, and action-strongly-bound data—including remote control trajectories, first-person videos, spatial maps, and operational sequences—these data do not naturally exist and must be generated through real interactions or high-fidelity simulations, making them scarcer and more expensive. Although simulation and synthetic data are helpful, they cannot replace real sensor-motion experience, which is why companies like Tesla and Figure must build their own remote operation data factories, and why third-party data annotation factories have emerged in Southeast Asia. In short: LLMs learn from ready-made data, while robots must "create" data through interaction with the physical world. In the next 5–10 years, the two will deeply integrate in the Vision–Language–Action model and Embodied Agent architecture—LLMs will handle high-level cognition and planning, while robots will execute in the real world, forming a bidirectional closed loop of data and action, jointly promoting AI from "language intelligence" to true general intelligence (AGI).
The core technology system of embodied intelligence can be viewed as a bottom-up intelligence stack: VLA (perception fusion), RL/IL/SSL (intelligent learning), Sim2Real (reality transfer), World Model (cognitive modeling), and multi-agent collaboration and memory reasoning (Swarm & Reasoning). Among them, VLA and RL/IL/SSL are the "engines" of embodied intelligence, determining its landing and commercialization; Sim2Real and World Model are key technologies connecting virtual training and real execution; multi-agent collaboration and memory reasoning represent a higher level of collective and metacognitive evolution.
Perception Understanding: Vision–Language–Action Model (VLA)
The VLA model integrates the three channels of vision (Vision)—language (Language)—action (Action), enabling robots to understand intentions from human language and translate them into specific operational behaviors. Its execution process includes semantic parsing, target recognition (locating target objects from visual input), and path planning and action execution, thus achieving a closed loop of "understanding semantics—perceiving the world—completing tasks," which is one of the key breakthroughs of embodied intelligence. Current representative projects include Google RT-X, Meta Ego-Exo, and Figure Helix, showcasing cutting-edge directions such as cross-modal understanding, immersive perception, and language-driven control.
Currently, VLA is still in its early stages and faces four core bottlenecks:
Semantic ambiguity and weak task generalization: The model struggles to understand vague, open-ended instructions;
Unstable alignment between vision and action: Perception errors are amplified in path planning and execution;
Scarcity and lack of standardization in multi-modal data: High costs of collection and annotation make it difficult to form a scalable data flywheel;
Challenges of time and space axes in long-duration tasks: Long task spans lead to insufficient planning and memory capabilities, while large spatial ranges require the model to reason about things "beyond the field of view," and the current VLA lacks stable world models and cross-space reasoning capabilities.
These issues collectively limit the cross-scenario generalization ability and large-scale landing process of VLA.
Intelligent Learning: Self-Supervised Learning (SSL), Imitation Learning (IL), and Reinforcement Learning (RL)
Self-Supervised Learning (SSL): Automatically extracts semantic features from perceptual data, allowing robots to "understand the world." Equivalent to teaching machines to observe and represent.
Imitation Learning (IL): Quickly masters basic skills by mimicking human demonstrations or expert examples. Equivalent to teaching machines to act like humans.
Reinforcement Learning (RL): Through a "reward-punishment" mechanism, robots optimize action strategies through continuous trial and error. Equivalent to teaching machines to grow through trial and error.
In embodied intelligence (Embodied AI), self-supervised learning (SSL) aims to enable robots to predict state changes and physical laws through perceptual data, thereby understanding the causal structure of the world; reinforcement learning (RL) is the core engine of intelligence formation, driving robots to master complex behaviors such as walking, grasping, and obstacle avoidance through interaction with the environment and trial-and-error optimization based on reward signals; imitation learning (IL) accelerates this process by using human demonstrations, allowing robots to quickly acquire action priors. The current mainstream direction is to combine the three to construct a hierarchical learning framework: SSL provides the representation foundation, IL imparts human priors, and RL drives policy optimization, balancing efficiency and stability, collectively forming the core mechanism of embodied intelligence from understanding to action.
Reality Transfer: Sim2Real — Bridging Simulation to Reality
Sim2Real (Simulation to Reality) allows robots to complete training in virtual environments and then transfer to the real world. It generates large-scale interactive data through high-fidelity simulation environments (such as NVIDIA Isaac Sim & Omniverse, DeepMind MuJoCo), significantly reducing training costs and hardware wear. Its core lies in narrowing the "simulation-reality gap," with main methods including:
Domain Randomization: Randomly adjusting parameters such as lighting, friction, and noise in simulation to enhance model generalization;
Physical Consistency Calibration: Using real sensor data to calibrate the simulation engine, enhancing physical realism;
Adaptive Fine-tuning: Rapid retraining in real environments to achieve stable transfer.
Sim2Real is a central link in the landing of embodied intelligence, enabling AI models to learn the closed loop of "perception—decision—control" in a safe, low-cost virtual world. Sim2Real has matured in simulation training (e.g., NVIDIA Isaac Sim, MuJoCo), but reality transfer is still limited by the Reality Gap, high computing power and annotation costs, and insufficient generalization and safety in open environments. Nevertheless, Simulation-as-a-Service (SimaaS) is becoming the lightest yet most strategically valuable infrastructure in the era of embodied intelligence, with business models including platform subscriptions (PaaS), data generation (DaaS), and security verification (VaaS).
Cognitive Modeling: World Model — The "Inner World" of Robots
World Model is the "inner brain" of embodied intelligence, allowing robots to internally simulate environments and the consequences of actions, achieving prediction and reasoning. By learning the dynamic laws of the environment, it constructs predictable internal representations, enabling agents to "rehearse" outcomes before execution, evolving from passive executors to active reasoners. Representative projects include DeepMind Dreamer, Google Gemini + RT-2, Tesla FSD V12, NVIDIA WorldSim, etc. Typical technical paths include:
Latent Dynamics Modeling: Compressing high-dimensional perception into latent state space;
Imagination-based Planning: Virtual trial-and-error and path prediction within the model;
Model-based RL: Using world models to replace real environments, reducing training costs.
World Model is at the theoretical forefront of embodied intelligence, representing the core path for robots to transition from "reactive" to "predictive" intelligence, but it is still limited by challenges such as complex modeling, unstable long-term predictions, and a lack of unified standards.
Collective Intelligence and Memory Reasoning: From Individual Action to Collaborative Cognition
Multi-Agent Systems and Memory & Reasoning represent two important directions in the evolution of embodied intelligence from "individual intelligence" to "collective intelligence" and "cognitive intelligence." Together, they support the collaborative learning and long-term adaptability of intelligent systems.
Multi-Agent Collaboration (Swarm / Cooperative RL):
Refers to multiple agents achieving collaborative decision-making and task allocation through distributed or cooperative reinforcement learning in a shared environment. This direction has a solid research foundation, such as the OpenAI Hide-and-Seek experiment demonstrating spontaneous cooperation and strategy emergence among multiple agents, and the DeepMind QMIX and MADDPG algorithms providing a cooperative framework for centralized training and decentralized execution. Such methods have been validated in scenarios like warehouse robot scheduling, inspection, and swarm control.
Memory and Reasoning
Focuses on enabling agents to possess long-term memory, situational understanding, and causal reasoning capabilities, which are key directions for achieving cross-task transfer and self-planning. Typical research includes DeepMind Gato (a multi-task agent unifying perception-language-control) and the DeepMind Dreamer series (imagination-based planning based on world models), as well as open-ended embodied agents like Voyager, which achieve continuous learning through external memory and self-evolution. These systems lay the foundation for robots to have the ability to "remember the past and infer the future."
Global Landscape of the Embodied Intelligence Industry: Cooperation and Competition Coexist
The global robotics industry is currently in a period of "cooperation dominance and deepening competition." China's supply chain efficiency, the U.S.'s AI capabilities, Japan's component precision, and Europe's industrial standards collectively shape the long-term landscape of the global robotics industry.
The United States maintains a leading position in cutting-edge AI models and software (DeepMind, OpenAI, NVIDIA), but this advantage has not extended to robotic hardware. Chinese manufacturers have a greater advantage in iteration speed and performance in real scenarios. The U.S. is promoting industrial return through the CHIPS Act and the Inflation Reduction Act (IRA).
China has formed a leading advantage in components, automated factories, and humanoid robots through large-scale manufacturing, vertical integration, and policy-driven initiatives, with outstanding hardware and supply chain capabilities. Companies like Unitree and UBTECH have achieved mass production and are extending towards intelligent decision-making layers. However, there is still a significant gap in algorithms and simulation training compared to the U.S.
Japan has long monopolized high-precision components and motion control technology, with a robust industrial system, but the integration of AI models is still in the early stages, and the pace of innovation is relatively steady.
South Korea stands out in the popularization of consumer-grade robots, led by companies like LG and NAVER Labs, and has a mature and strong service robot ecosystem.
Europe has a well-established engineering system and safety standards, with companies like 1X Robotics remaining active in R&D, but some manufacturing processes have migrated, and the focus of innovation has shifted towards collaboration and standardization.
Robotics × AI × Web3: Narrative Vision and Real Pathways
In 2025, a new narrative emerges in the Web3 industry that integrates with robotics and AI. Although Web3 is seen as the underlying protocol for a decentralized machine economy, its combined value and feasibility at different levels still show significant differentiation:
In the hardware manufacturing and service layer, capital-intensive and weak data closed loops mean that Web3 can currently only play a supporting role in peripheral areas such as supply chain finance or equipment leasing;
In the simulation and software ecosystem layer, the compatibility is higher, as simulation data and training tasks can be verified on-chain, and agents and skill modules can also be tokenized through NFTs or Agent Tokens;
In the platform layer, decentralized labor and collaboration networks are showing the greatest potential—Web3 can gradually build a trusted "machine labor market" through integrated mechanisms of identity, incentives, and governance, laying the institutional groundwork for the future machine economy.
From a long-term vision perspective, the collaboration and platform layers are the most valuable directions for the integration of Web3 with robotics and AI. As robots gradually acquire perception, language, and learning capabilities, they are evolving into intelligent individuals capable of autonomous decision-making, collaboration, and creating economic value. For these "intelligent laborers" to truly participate in the economic system, they still need to overcome four core thresholds of identity, trust, incentives, and governance.
At the identity layer, machines need to have verifiable and traceable digital identities. Through Machine DID, each robot, sensor, or drone can generate a unique verifiable "ID card" on-chain, binding its ownership, behavior records, and permission scope, enabling secure interactions and responsibility delineation.
At the trust layer, the key is to make "machine labor" verifiable, measurable, and priceable. By leveraging smart contracts, oracles, and auditing mechanisms, combined with physical work proofs (PoWP), trusted execution environments (TEE), and zero-knowledge proofs (ZKP), the authenticity and traceability of the task execution process can be ensured, giving economic accounting value to machine behavior.
At the incentive layer, Web3 achieves automatic settlement and value transfer between machines through a token incentive system, account abstraction, and state channels. Robots can complete computing power leasing and data sharing through micropayments, and ensure task fulfillment through staking and penalty mechanisms; with the help of smart contracts and oracles, a decentralized "machine collaboration market" can also be formed without human scheduling.
At the governance layer, once machines possess long-term autonomous capabilities, Web3 provides a transparent and programmable governance framework: using DAO governance to jointly decide system parameters, and maintaining security and order through multi-signature and reputation mechanisms. In the long run, this will promote the machine society towards the stage of "algorithmic governance"—humans set goals and boundaries, while contracts maintain incentives and balance among machines.
The ultimate vision for the integration of Web3 and robotics: a real-world evaluation network—a "real-world reasoning engine" composed of distributed robots, continuously testing and benchmarking model capabilities in diverse and complex physical scenarios; and a robot labor market—robots executing verifiable real tasks globally, obtaining rewards through on-chain settlement, and reinvesting value into computing power or hardware upgrades.
From a practical perspective, the integration of embodied intelligence and Web3 is still in the early exploratory stage, with decentralized machine intelligence economies primarily remaining at the narrative and community-driven level. The feasible integration directions in reality mainly manifest in the following three aspects:
(1) Data Crowdsourcing and Verification — Web3 encourages contributors to upload real-world data through on-chain incentives and traceability mechanisms;
(2) Global Long-Tail Participation — Cross-border micropayments and micro-incentive mechanisms effectively reduce the costs of data collection and distribution;
(3) Financialization and Collaborative Innovation — The DAO model can promote the assetization of robots, the certification of revenue, and settlement mechanisms between machines.
Overall, the short term mainly focuses on data collection and incentive layers; the mid-term is expected to achieve breakthroughs in "stablecoin payments + long-tail data aggregation" and RaaS assetization and settlement layers; in the long term, if humanoid robots achieve large-scale adoption, Web3 may become the institutional foundation for machine ownership, revenue distribution, and governance, driving the formation of a truly decentralized machine economy.
Web3 Robotics Ecosystem Map and Selected Cases
Based on the three criteria of "verifiable progress, technical openness, and industry relevance," we have sorted current representative projects in the Web3 × Robotics space and categorized them according to a five-layer architecture: Model Intelligence Layer, Machine Economy Layer, Data Collection Layer, Perception and Simulation Infrastructure Layer, and Robot Asset Revenue Layer. To maintain objectivity, we have excluded projects that are clearly "riding the wave" or lack sufficient information; any omissions are welcome for correction.
Model Intelligence Layer
Openmind - Building Android for Robots
OpenMind is an open-source operating system (Robot OS) aimed at embodied intelligence (Embodied AI) and robot control, with the goal of building the world's first decentralized robot operating environment and development platform. The core of the project includes two major components:
OM1: A modular open-source AI runtime layer built on ROS2, used to orchestrate perception, planning, and action pipelines, serving both digital and physical robots;
FABRIC: A distributed coordination layer that connects cloud computing power, models, and real robots, allowing developers to control and train robots in a unified environment.
The core of OpenMind is to act as an intelligent intermediary layer between LLMs (large language models) and the robotic world, enabling language intelligence to truly transform into embodied intelligence, constructing an intelligent framework from understanding (Language → Action) to alignment (Blockchain → Rules).
The multi-layer system of OpenMind achieves a complete collaborative closed loop: humans provide feedback and annotations through the OpenMind App (RLHF data), the Fabric Network is responsible for identity verification, task allocation, and settlement coordination, OM1 Robots execute tasks and follow the "Robot Constitution" on the blockchain to complete behavior audits and payments, thus realizing a decentralized machine collaboration network of human feedback → task collaboration → on-chain settlement.
Project Progress and Reality Assessment
OpenMind is in the early stage of "technology operational, business not yet landed." The core system OM1 Runtime has been open-sourced on GitHub, can run on multiple platforms, and supports multimodal input, achieving task understanding from language to action through a natural language data bus (NLDB). It has high originality but is still experimental, and the Fabric network and on-chain settlement have only completed interface layer design.
In terms of ecology, the project has collaborated with open hardware such as Unitree, Ubtech, TurtleBot, and universities like Stanford, Oxford, and Seoul Robotics, mainly for educational and research validation, with no industrialization yet. The app has launched a test version, but the incentive and task functions are still in the early stages.
Regarding the business model, OpenMind has built a three-layer ecosystem of OM1 (open-source system) + Fabric (settlement protocol) + Skill Marketplace (incentive layer), currently with no revenue, relying on approximately $20 million in early financing (Pantera, Coinbase Ventures, DCG). Overall, it is technically advanced but still in the early stages of commercialization and ecology. If Fabric successfully lands, it is expected to become the "Android of the embodied intelligence era," but it has a long cycle, high risk, and strong dependence on hardware.
CodecFlow - The Execution Engine for Robotics
CodecFlow is a decentralized execution layer protocol (Fabric) based on the Solana network, aimed at providing on-demand operating environments for AI agents and robotic systems, allowing each agent to have an "Instant Machine." The core of the project consists of three major modules:
Fabric: A cross-cloud computing power aggregation layer (Weaver + Shuttle + Gauge) that can generate secure virtual machines, GPU containers, or robot control nodes for AI tasks within seconds;
optr SDK: An agent execution framework (Python interface) for creating operable desktops, simulations, or real robots;
Token Incentives: An on-chain incentive and payment layer that connects computing providers, agent developers, and automated task users, forming a decentralized computing power and task market.
The core goal of CodecFlow is to create a "decentralized execution base for AI and robot operators," allowing any agent to run safely in any environment (Windows / Linux / ROS / MuJoCo / robot controllers), achieving a universal execution architecture from computing power scheduling (Fabric) → system environment (System Layer) → perception and action (VLA Operator).
Project Progress and Reality Assessment
The early versions of the Fabric framework (Go) and optr SDK (Python) have been released, allowing isolated computing instances to be launched in web or command-line environments. The Operator market is expected to launch by the end of 2025, positioning itself as the decentralized execution layer for AI computing power, primarily serving AI developers, robotics research teams, and automation operation companies.
Machine Economy Layer
BitRobot - The World’s Open Robotics Lab
BitRobot is a decentralized research and collaboration network (Open Robotics Lab) focused on embodied intelligence (Embodied AI) and robot R&D, jointly initiated by FrodoBots Labs and Protocol Labs. Its core vision is to define and verify the true contributions of each robotic task through an open architecture of "Subnets + Incentive Mechanisms + Verifiable Robotic Work (VRW)," with core functions including:
Defining and verifying the true contributions of each robotic task through the VRW (Verifiable Robotic Work) standard;
Endowing robots with on-chain identity and economic responsibility through the ENT (Embodied Node Token);
Organizing cross-regional collaboration in research, computing power, equipment, and operators through Subnets;
Achieving "human-machine co-governance" through incentive decision-making and research governance with Senate + Gandalf AI.
Since the release of its white paper in 2025, BitRobot has operated multiple subnets (such as SN/01 ET Fugi, SN/05 SeeSaw by Virtuals Protocol), achieving decentralized remote control and real-world data collection, and launched the $5M Grand Challenges fund to promote global model development research competitions.
peaq – The Economy of Things
peaq is a Layer-1 blockchain designed for the machine economy, providing machine identities, on-chain wallets, access control, and nanosecond-level time synchronization (Universal Machine Time) for millions of robots and devices. Its Robotics SDK enables developers to make robots "machine economy ready" with minimal code, achieving interoperability and interaction across vendors and systems.
Currently, peaq has launched the world's first tokenized robot farm and supports over 60 real-world machine applications. Its tokenization framework helps robotics companies raise funds for capital-intensive hardware and expands participation from traditional B2B/B2C to a broader community level. With a protocol-level incentive pool funded by network fees, peaq can subsidize new device access and support developers, thus forming an economic flywheel that accelerates the expansion of robotics and physical AI projects.
Data Collection Layer
Aiming to solve the scarcity and high cost of high-quality real-world data in embodied intelligence training. It collects and generates human-machine interaction data through various paths, including remote control (PrismaX, BitRobot Network), first-person perspective and motion capture (Mecka, BitRobot Network, Sapien, Vader, NRN), as well as simulation and synthetic data (BitRobot Network), providing a scalable and generalizable training foundation for robotic models.
It is important to clarify that Web3 is not adept at "producing data"—in terms of hardware, algorithms, and collection efficiency, Web2 giants far surpass any DePIN project. Its true value lies in reshaping the distribution and incentive mechanisms of data. Based on a "stablecoin payment network + crowdsourcing model," it achieves low-cost micropayments, contribution traceability, and automatic profit sharing through a permissionless incentive system and on-chain verification mechanisms. However, open crowdsourcing still faces challenges in quality and demand closure—data quality is uneven, and there is a lack of effective verification and stable buyers.
PrismaX
PrismaX is a decentralized remote control and data economy network focused on embodied intelligence (Embodied AI), aiming to build a "global robot labor market" where human operators, robotic devices, and AI models co-evolve through an on-chain incentive system. The core of the project includes two major components:
Teleoperation Stack— A remote control system (browser/VR interface + SDK) that connects global robotic arms and service robots, enabling real-time human control and data collection;
Eval Engine— A data evaluation and verification engine (CLIP + DINOv2 + optical flow semantic scoring) that generates quality scores for each operational trajectory and settles them on-chain.
PrismaX transforms human operational behavior into machine learning data through a decentralized incentive mechanism, constructing a complete closed loop from remote control → data collection → model training → on-chain settlement, realizing a circular economy where "human labor is data assets."
Project Progress and Reality Assessment
PrismaX launched its test version in August 2025 (gateway.prismax.ai), allowing users to remotely control robotic arms to perform grasping experiments and generate training data. The Eval Engine is already running internally. Overall, PrismaX has a high degree of technical implementation and a clear positioning, serving as a key intermediary layer connecting "human operation × AI model × blockchain settlement." Its long-term potential is expected to become a "decentralized labor and data protocol of the embodied intelligence era," but it still faces scaling challenges in the short term.
BitRobot Network
BitRobot Network achieves multi-source data collection through its subnets, including video, remote control, and simulation. SN/01 ET Fugi allows users to remotely control robots to complete tasks, collecting navigation and perception data in a "real-world Pokémon Go-style" interaction. This gameplay has led to the creation of the FrodoBots-2K dataset, one of the largest open-source human-robot navigation datasets currently used by institutions such as UC Berkeley RAIL and Google DeepMind. SN/05 SeeSaw (Virtual Protocol) crowdsources first-person perspective video data on a large scale in real environments using iPhones. Other announced subnets, such as RoboCap and Rayvo, focus on using low-cost physical devices to collect first-person perspective video data.
Mecka
Mecka is a robotics data company that crowdsources first-person perspective video, human motion data, and task demonstrations through gamified mobile collection and custom hardware devices, aiming to build large-scale multimodal datasets to support the training of embodied intelligence models.
Sapien
Sapien is a crowdsourcing platform centered on "human motion data driving robotic intelligence," collecting human action, posture, and interaction data through wearable devices and mobile applications for training embodied intelligence models. The project aims to build the world's largest human motion data network, making natural human behavior the foundational data source for robot learning and generalization.
Vader
Vader collects first-person perspective video and task demonstrations through its real-world MMO application EgoPlay: users record daily activities from a first-person perspective and earn $VADER rewards. Its ORN data pipeline can convert raw POV footage into privacy-processed structured datasets, including action labels and semantic descriptions, which can be directly used for humanoid robot strategy training.
NRN Agents
A gamified embodied RL data platform that crowdsources human demonstration data through browser-based robot control and simulation competitions. NRN generates long-tail behavior trajectories through "competitive" tasks for imitation learning and continuous reinforcement learning, serving as scalable data primitives to support sim-to-real strategy training.
Comparison of Embodied Intelligence Data Collection Layer Projects
Perception and Simulation (Middleware & Simulation)
The perception and simulation layer provides the core infrastructure for robots to connect the physical world with intelligent decision-making, including capabilities such as localization, communication, spatial modeling, and simulation training. It serves as the "intermediate skeletal structure" for building large-scale embodied intelligence systems. Currently, this field is still in the early exploratory stage, with various projects forming differentiated layouts in high-precision localization, shared spatial computing, protocol standardization, and distributed simulation, without a unified standard or interoperable ecosystem emerging yet.
Middleware and Spatial Infrastructure (Middleware & Spatial Infra)
The core capabilities of robots—navigation, localization, connectivity, and spatial modeling—constitute the key bridge connecting the physical world with intelligent decision-making. Although broader DePIN projects (Silencio, WeatherXM, DIMO) have begun to mention "robots," the following projects are most directly related to embodied intelligence.
RoboStack – Cloud-Native Robot Operating Stack
RoboStack is a cloud-native robot middleware that achieves real-time scheduling, remote control, and cross-platform interoperability of robot tasks through RCP (Robot Context Protocol), providing cloud simulation, workflow orchestration, and agent access capabilities.
GEODNET – Decentralized GNSS Network
GEODNET is a global decentralized GNSS network that provides centimeter-level RTK high-precision positioning. Through distributed base stations and on-chain incentives, it offers real-time "geographic reference layers" for drones, autonomous driving, and robots.
Auki – Posemesh for Spatial Computing
Auki has built a decentralized Posemesh spatial computing network that generates real-time 3D environmental maps through crowdsourced sensors and computing nodes, providing shared spatial references for AR, robot navigation, and multi-device collaboration. It is a key infrastructure connecting virtual spaces with real-world scenarios, promoting the integration of AR × Robotics.
Tashi Network — Real-Time Mesh Collaboration Network for Robots
A decentralized real-time mesh network that achieves sub-30ms consensus, low-latency sensor exchange, and multi-robot state synchronization. Its MeshNet SDK supports shared SLAM, collective collaboration, and robust map updates, providing a high-performance real-time collaboration layer for embodied AI.
Staex — Decentralized Connectivity and Telemetry Network
A decentralized connectivity layer originating from the research department of Deutsche Telekom, providing secure communication, trusted telemetry, and routing capabilities from devices to the cloud, enabling robot fleets to reliably exchange data and collaborate across different operators.
Simulation and Training Systems (Distributed Simulation & Learning)
Gradient - Towards Open Intelligence
Gradient is an AI laboratory building "Open Intelligence," dedicated to achieving distributed training, inference, validation, and simulation based on decentralized infrastructure. Its current tech stack includes Parallax (distributed inference), Echo (distributed reinforcement learning and multi-agent training), and Gradient Cloud (enterprise AI solutions). In the robotics direction, the Mirage platform provides distributed simulation, dynamic interactive environments, and large-scale parallel learning capabilities for training embodied intelligence, accelerating the deployment of world models and general strategies. Mirage is exploring potential collaboration directions with NVIDIA regarding its Newton engine.
Robot Asset Revenue Layer (RobotFi / RWAiFi)
This layer focuses on transforming robots from "productive tools" into "financializable assets," constructing the financial infrastructure of the machine economy through asset tokenization, revenue distribution, and decentralized governance. Representative projects include:
XmaquinaDAO – Physical AI DAO
XMAQUINA is a decentralized ecosystem that provides global users with high liquidity participation channels for top humanoid robots and embodied intelligence companies, bringing opportunities that were once exclusive to venture capital institutions on-chain. Its token DEUS serves as both a liquidity index asset and a governance vehicle, coordinating treasury allocation and ecological development. Through the DAO Portal and Machine Economy Launchpad, the community can participate in the tokenization and structured on-chain participation of machine assets, jointly owning and supporting emerging Physical AI projects.
GAIB – The Economic Layer for AI Infrastructure
GAIB is dedicated to providing a unified economic layer for physical AI infrastructure such as GPUs and robots, connecting decentralized capital with real AI infrastructure assets to build a verifiable, composable, and revenue-generating intelligent economic system.
In the robotics sector, GAIB does not "sell robot tokens," but instead financializes robotic devices and operational contracts (RaaS, data collection, remote operation, etc.) on-chain to achieve the transformation from "real cash flow → on-chain composable revenue assets." This system encompasses hardware financing (leasing / collateral), operational cash flow (RaaS / data services), and data flow revenue (licensing / contracts), making robotic assets and their cash flows measurable, priceable, and tradable.
GAIB uses AID / sAID as the settlement and revenue vehicle, ensuring robust returns through a structured risk control mechanism (over-collateralization, reserves, and insurance), and long-term access to DeFi derivatives and liquidity markets, forming a financial closed loop from "robotic assets" to "composable revenue assets." The goal is to become the economic backbone of intelligence in the AI era.
Web3 Robotics Ecosystem Map
Summary and Outlook: Real Challenges and Long-term Opportunities
From a long-term vision, the integration of robotics × AI × Web3 aims to build a decentralized machine economy system (DeRobot Economy), promoting embodied intelligence from "standalone automation" to "verifiable, settleable, and governable" networked collaboration. Its core logic is to form a self-circulating mechanism through "Token → Deployment → Data → Value Redistribution," enabling robots, sensors, and computing nodes to achieve rights confirmation, trading, and profit sharing.
However, from a realistic perspective, this model is still in the early exploratory phase, far from forming stable cash flows and scalable business closed loops. Most projects remain at the narrative level, with limited actual deployment. Robotics manufacturing and operation are capital-intensive industries, and relying solely on token incentives is insufficient to support infrastructure expansion; while on-chain financial designs have composability, they have yet to resolve the risk pricing and revenue realization issues of real assets. Therefore, the so-called "machine network self-circulation" remains idealized, and its business model awaits real-world validation.
Model & Intelligence Layer is currently the most valuable long-term direction. OpenMind, as a representative open-source robotic operating system, attempts to break closed ecosystems and unify multi-robot collaboration and language-to-action interfaces. Its technical vision is clear, and the system is complete, but the engineering workload is enormous, and the validation cycle is long, with no industry-level positive feedback yet formed.
Machine Economy Layer is still in the preliminary stage, with a limited number of robots in reality, and the DID identity and incentive network have yet to form a self-consistent cycle. We are still far from a "machine labor economy." Only after embodied intelligence achieves large-scale deployment will the economic effects of on-chain identity, settlement, and collaboration networks truly manifest.
Data Layer has the lowest threshold for entry, but is currently the closest to commercially viable directions. The data collection for embodied intelligence requires high spatiotemporal continuity and action semantic accuracy, determining its quality and reusability. Balancing "crowdsourcing scale" and "data reliability" is a core challenge in the industry. PrismaX first locks in B-end demand and then distributes tasks for collection and verification, providing a replicable template to some extent, but the ecological scale and data trading still require time to accumulate.
Middleware & Simulation Layer is still in the technical validation phase, lacking unified standards and interfaces, and has yet to form an interoperable ecosystem. Simulation results are difficult to standardize and transfer to real environments, limiting Sim2Real efficiency.
RobotFi / RWAiFi Layer mainly plays a supportive role in supply chain finance, equipment leasing, and investment governance in Web3, enhancing transparency and settlement efficiency, rather than reshaping industrial logic.
Of course, we believe that the intersection of robotics × AI × Web3 still represents the origin of the next generation of intelligent economic systems. It is not only a fusion of technological paradigms but also an opportunity for the reconstruction of production relations: when machines possess identity, incentives, and governance mechanisms, human-machine collaboration will shift from localized automation to networked autonomy. In the short term, this direction remains primarily narrative and experimental, but the institutional and incentive frameworks it lays down are paving the way for the economic order of future machine societies. From a long-term perspective, the combination of embodied intelligence and Web3 will reshape the boundaries of value creation—making intelligent agents truly verifiable, collaborative, and revenue-generating economic entities.
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