Is This the Year of the Robot? A Deep Dive into Robotics Projects

In his speech at Davos earlier this year, Musk once again reiterated that provocative prediction — in the future, the number of robots on Earth will surpass that of humans.

Evidently, AI and robots have essentially become the only two global tech topics: one being the ever-approaching General Artificial Intelligence (AGI) threshold, and the other being robots stepping out of the lab, attempting to fully take over human physical labor. Similarly, apart from the AI concept, this year's key focus track in the cryptocurrency industry also includes embodied intelligence. Here are some projects in the Robotic track worth paying attention to.

OpenMind

On August 4, 2025, according to official sources, Silicon Valley-based intelligent machine infrastructure company OpenMind announced the completion of a $20 million financing round, led by Pantera Capital, with participation from Ribbit, Sequoia China, Coinbase Ventures, DCG, Lightspeed Faction, Anagram, Pi Network Ventures, Topology, Primitive Ventures, and Amber Group, among many other institutions and well-known angel investors.

OpenMind is helping robots think, learn, and work by developing open-source software. The native open-source AI robot operating system OM1 allows the configuration and deployment of AI Agents in both the digital and physical worlds. Users can create an AI character, run it in the cloud, or run it on a physical robot in the real world.

In simple terms, what OpenMind is doing with OM1 is equivalent to creating an "AI brain" for robots. This "AI brain" can have multiple AI Agents working together, interact with multiple LLMs, and work with data from various sources (such as helping users post on social media). Because OM1 is open source, this is also a highly adaptable robot operating system, like the Android system on phones, agnostic to hardware.

Additionally, OpenMind has a blockchain-based robot identity network called FABRIC, designed to provide a verifiable trust layer for humans and robots to share. Humans can earn badges through map sharing of location data, assessing robot behavior, and development on it. For robots, every robot equipped with the OM1 system will join the FABRIC network, thereby possessing a unique verifiable identity, and enabling robot commands, operation logs, ownership, and other related behaviors to be traceable on the chain.

In December 2025, OpenMind and the stablecoin issuer Circle jointly announced the launch of a robot autonomous payment system based on the x402 protocol. As robots' capabilities improve, they will no longer be just tools for executing tasks but will begin to play the role of autonomous economic agents. They will need to purchase computing power, data, skills, and even hire other robots or humans to complete complex tasks.

CodecFlow

CodecFlow provides a unified platform that can seamlessly run on cloud, edge, desktop, and robot hardware, while supporting popular APIs and legacy systems. The platform normalizes various robot sensor inputs into a common format and modularizes more complex robot actions, allowing development teams or users to design robots without starting from scratch. The perception, decision-making, and control between robots also interact through the network, rather than being fragmented or specific to a single hardware platform.

AI-driven operators respond to changes in the software's UI or the robot's environment through perception and real-time reasoning, addressing the fragility of traditional robot automation processes that rely too heavily on pre-scripted behavior and are vulnerable even to minor changes. In short, it involves capturing screenshots, camera images, or sensor data and then using AI to process this external input data to derive observations or commands, ultimately executing decisions through user interface interactions.

Peaq

On March 27, 2025, the DePIN Layer1 protocol Peaq completed a $15 million funding round, led by Generative Ventures and Borderless Capital, with participation from Spartan Group, HV Capital, CMCC Global, Animoca Brands, Moonrock Capital, Fundamental Labs, TRGC, DWF Labs, Crit Ventures, Cogitent Ventures, NGC Ventures, Agnostic Fund, Altana Wealth, and others.

Although originally focused on DePIN, Peaq released its Robotics SDK in September last year, enabling robots to obtain autonomous identity, make payments and receipts, validate data, and participate in the on-chain network economy. Now, any robot compatible with the ROS2 system can join the Peaq network economy, transacting with humans or other robots using its common standards.

In addition, peaq launched a robot RWA project called "RoboFarm" on DualMint last year, where they established a robot farm in Hong Kong achieving 80% automation of agricultural production through robots. The grown lettuce, spinach, and kale are sold in Hong Kong. The estimated annualized return for NFT holders is about 18%.

Axis Robotics

Axis Robotics is dedicated to building a physically intelligent distributed expansion infrastructure. They firmly believe that "Simulation First" is the best path to breakthrough the robot data scarcity and model generalization bottleneck. Through low-cost, scalable data collection combined with a unique data augmentation engine, they have achieved a triple leap in data quality, richness, and scale. Additionally, each data asset has trustworthy on-chain provenance, collectively establishing a core fuel repository to drive the evolution of Generalized Robot Intelligence (RGI).

Axis has innovated the way robot training data is provided. Most other projects that "input/provide robot training data" on the market mobilize users to shoot and upload videos of specific actions completed in reality through devices such as phones and smart glasses to achieve low-barrier, global user participation. While this data acquisition cost is low, the physical authenticity of video-captured data is insufficient, lacking depth information, and unable to ensure the continuity and accuracy of 3D data.

Axis addresses this pain point through "Simulation Simulation," where in a simulated environment, models can complete tasks in more rigorous virtual conditions across diverse simulation scenarios (lighting, angles, friction, dynamics, etc.), enabling them to gain strong generalization capabilities. Axis employs a Hybrid Strategy, combining scarce real data with massive synthetic data. Utilizing GPU-accelerated metadata enhancement technology, they achieve significant variations in lighting, textures, and physical properties within a single scene. Virtual scenes are not static or hardcoded but flexible and adjustable. Code can generate countless scenes, allowing robots to face more rigorous and comprehensive challenges in various scenarios. The cost of generating scenes is low, yet the output quantity is high. The effectiveness of this approach, using abundant data to converge toward an optimal solution, has been partially validated by giants like Google and NVIDIA.

Axis has completed the first open and public simulation-based robot learning project for the community, "Little Prince's Rose." In the "Little Prince's Rose" project, users make the robot successfully water a flower in a simulated environment through a web page. Through collecting and analyzing user interactions, the robot learns to water the flower. Users can remotely operate the robot via a web page, maintaining the low-cost, low-barrier approach of video upload capture while building a native 3D-aware VLA (Vision-Language-Action) base model for the robot, enhancing the robot's lack of three-dimensional spatial thinking ability in video data input channels.

The 「Little Prince's Rose」 project, only 5 days after launch, received over tens of thousands of high-quality, strategy-training-ready effective trajectories from ordinary users worldwide without a robotics background through engaging experiences. Leveraging this data set, Axis successfully trained a strategy model and accomplished the real-world reproduction of the Franka robotic arm. This milestone signifies Axis has completed the full-stack closed loop of "task generation -> community collection -> data augmentation -> model training -> real-world deployment."

One hour of real-world data can translate into 1000 hours of training data, significantly reducing the cost required for robot model generalization.

During the Chinese New Year Beta testing period, in just 5 days, 18,000 participants without a robotics background completed 27 new tasks on Axis, contributing over 100,000 data trajectories. The test successfully supported high task-level randomization and validated compatibility with various forms of assets such as wheeled robots, dual-arm robots, and others.

Axis's core product is set to be officially launched in late March, with plans to open-source the world's largest purely simulated dataset based on the Franka robotic arm by late April or early May, fully meeting the needs for strategy and model training. Additionally, as a robot track project originating from Crypto-AI, Axis has begun exploring and promoting external industry landing, collaborating with multiple leading customers in various sectors to accelerate commercialization: collaborating with a certain automaker to drive the landing of production automation solutions; reaching a cooperation consensus with a pre-IPO computing power company in the direction of virtual assets and the world model; and establishing deep cooperative relationships with many self-entity enterprises in key areas such as virtual simulation data collection and model training. All of these initiatives demonstrate the rare externalities of the Crypto project.

GEODNET

A decentralized network providing centimeter-level real-time dynamic positioning data for drones, robots, etc., with over 21,000 active base stations in 150+ countries. In the past year, the project has generated revenue exceeding 7 million USD and has shown a quarterly growth trend.

Although this project is more commonly classified under DePIN, as robotic technology becomes more widely used in real-life applications, the demand for high-precision real-time positioning data is expected to broaden. In February 2025, Multicoin announced it would lead an $8 million purchase of $GEDO tokens worth $8 million from the GEODNET Foundation.

BitRobot

The BitRobot Network, developed jointly by FrodoBots Lab and Protocol Labs, aims to achieve distributed robot work and collaboration. Its key components include: Verifiable Robot Work (VRW) for defining and validating robot tasks (a quantifiable measure of network rewards), Device Node Tokens (ENT) for device ownership and network access (the robot's unique identity within the system, existing as an NFT), and a subnet serving as the layer for task execution operations (a resource cluster that creates value for the BitRobot Network).

On February 14, 2025, FrodoBots Lab announced the completion of a $6 million seed round financing, bringing the total funding to $8 million.

FrodoBots Lab also sells robots, such as the Earth Rovers resembling real-life Mario Karts, priced at $249. Players remote-control their robots through a browser in a global treasure hunt game called ET Fugi, providing data for researchers to deploy and test their latest AI navigation models. ET Fugi is also the first subnet of BitRobot.

Another game robot, Octo Arms, will be launched in the future, allowing players to remotely control a robotic arm to complete various 3D puzzle games and competitions.

The concept of "subnet" in this robot network is relatively abstract. Simply put, any cluster (or specific project/event the cluster needs to undertake) that can contribute to the overall network ecosystem is considered a subnet. For example, the ET Fugi game mentioned above and SeeSaw launched by Virtuals are subnets.

SeeSaw

The 5th subnet of BitRobot, an application for robot training data sharing launched by Virtuals in October of last year. In SeeSaw, users shoot videos of their daily activities, upload them to complete tasks, and earn rewards. These video data of daily activities from global users, such as tying shoelaces and folding clothes, will be used to train robots.

Auki

Auki's decentralized machine perception network Posemesh is used to connect humans, devices, and AI. At its core is a DePIN (Decentralized Physical Entity Network) architecture, allowing devices like robots and AR glasses to share location and sensor data in real-time, co-building a collaborative spatial understanding of the physical world. This can provide a shared spatial view for robots, AR, and AI.

Multiple node roles were designed based on the Posemesh protocol. The computation nodes provide computing power, the motion nodes (robot terminals) upload location information and sensor data, the reconstruction nodes generate 3D map models based on this information, and the domain nodes manage the 3D space. Each node receives $AUKI token incentives based on contributions, driving a self-evolving machine vision network.

This network emphasizes privacy protection, avoiding a single entity monitoring users' private space. It can be applied in various scenarios such as retail (product placement optimization), property management (asset tracking), exhibition navigation, architectural design, and other scenarios.

Their Cactus AI spatial computing platform has already conducted proactive pilots with Toyota Material Handling and the Swedish supermarket Stora Coop.

XMAQUINA

Empowering retail investors to participate in robot company investments through a DAO. The DAO raised $10 million by gradually selling its $DEUS tokens. Currently, the DAO has used the auction proceeds to purchase shares of 6 companies in the robotics field, including Apptronik, Figure AI, Agility Robotics, 1X Tech, NEURA Robotics, and Robotico. Some investments have started to become profitable, with individual returns exceeding 100%.

PrismaX

On June 17, 2025, PrismaX announced the completion of a $11 million funding round, with investors including a16z CSX, Volt Capital, Blockchain Builders Fund, Stanford Blockchain Accelerator, and Virtuals.

PrismaX is building an open coordination layer that connects remote operators, robot users, and robot companies. Operators can connect with users, remotely control robots to perform real tasks, collect valuable data, and request practical services such as logistics and advertising.

PrismaX also features a protocol for remotely operating robots, where companies can find experienced robot operators capable of handling complex tasks. Operators can choose to stake network tokens to increase trust and access opportunities for high-yield tasks. Stakers' earnings are not only related to the amount staked but also to their work quality. As work efficiency improves, they receive additional rewards.

The data accumulated from remote operations will be used to train robots to enhance their autonomy, which will in turn increase the efficiency of remote operators, ultimately achieving a high degree or even full autonomy of the robots.

NRN Agents

NRN has evolved from AI Agent real-time training in the AI Arena blockchain game. On October 28, 2021, developer ArenaX Labs announced the completion of a $5 million seed round, led by Paradigm Capital with participation from Framework Venture Partners. On January 9, 2024, ArenaX Labs announced the completion of a new $6 million funding round led by Framework Ventures, with participation from SevenX Ventures, FunPlus/Xterio, and Moore Strategic Ventures.

Although it is generally a process of collecting data and enhancing robot learning, NRN leverages its rich experience in the gaming field to provide a browser-based experience, transforming robot data collection into a game. Users can intuitively control simulated robots through the browser. During the game, the behavior data generated by user operations is used to train real-world robot systems.

At this stage, the project will focus on the robotic arm (RME-1) to validate data collection, real-time learning, and adaptability.