Will this year be the Year of Robots? An article reviewing projects in the robotics sector

Title: Will This Year Be the Year of Robots? An Overview of Projects in the Robotics Sector

Author: Cookie

Source:

Repost: Mars Finance

In Elon Musk’s speech at Davos earlier this year, he reiterated his provocative prophecy — in the future, the number of robots on Earth will surpass that of humans.

Clearly, AI and robotics are now the two hottest tech topics worldwide: one is general artificial intelligence approaching AGI thresholds, and the other is robots emerging from labs, aiming to fully take over human physical labor. Besides AI concepts, the cryptocurrency industry’s key sectors this year also include embodied intelligence. Below are some noteworthy projects in the Robotic sector.

OpenMind

On August 4, 2025, according to official sources, Silicon Valley-based intelligent machine infrastructure company OpenMind announced a $20 million funding 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 others, along with several well-known angel investors.

OpenMind develops open-source software to help robots think, learn, and work. Its native open-source AI robot operating system OM1 enables configuration and deployment of AI agents in both digital and physical worlds. Users can create AI characters that run in the cloud or on physical robots in the real world.

Simply put, OpenMind’s OM1 is like giving robots an “AI brain.” This “AI brain” can work collaboratively with multiple AI agents, interact with various large language models (LLMs), and fetch data from multiple sources (e.g., posting on social media). Since OM1 is open-source, it’s a highly adaptable robot OS, similar to Android for smartphones, hardware-agnostic.

Additionally, OpenMind has a chain-based robot identity network called FABRIC, aiming to share a verifiable trust layer between humans and robots. Humans can share location data, evaluate robot behavior, and earn badges through map sharing, while every robot loaded with OM1 joins the FABRIC network, gaining a unique verifiable identity. This allows command logs, operation records, ownership, and related activities to be traceable on the blockchain.

By December 2025, OpenMind and stablecoin issuer Circle jointly announced the launch of an autonomous robot payment system based on the x402 protocol. As robots’ capabilities grow, they will no longer be just task executors but will begin to act as autonomous economic entities, purchasing computing power, data, skills, or even hiring other robots or humans for complex tasks.

CodecFlow

CodecFlow offers a unified platform that seamlessly runs on cloud, edge, desktop, and robot hardware, supporting popular APIs and traditional systems. The platform standardizes sensor inputs into a universal format and modularizes complex robot actions, enabling development teams or users to avoid designing robots from scratch. Perception, decision-making, and control among robots can influence each other over the network, rather than being fragmented or hardware-specific.

AI-driven operators respond to UI changes or environmental shifts in real-time through perception and reasoning, addressing the fragility of traditional automation that relies heavily on pre-written scripts. In essence, it captures screenshots, camera feeds, or sensor data, then uses AI to process external inputs for observation or commands, ultimately executing decisions via user interface interactions.

Peaq

On March 27, 2025, 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, and Altana Wealth.

Although initially focused on DePIN narratives, Peaq released a Robotics SDK in September last year, enabling robots to obtain autonomous identities, make payments, verify data, and connect to on-chain network economies. Now, any robot compatible with ROS2 can join the Peaq network economy, using its standard protocols to trade with humans or other robots.

Last year, Peaq also launched a robot RWA project called “RoboFarm” on DualMint, establishing a robot farm in Hong Kong that automates 80% of agricultural production through robots. The produce—lettuce, spinach, and kale—is sold in Hong Kong. NFT holders are expected to earn an annualized yield of about 18%.

Axis Robotics

Axis Robotics focuses on building embodied intelligence (Physical AI) distributed infrastructure. They believe that a “Simulation First” approach is the best way to overcome data scarcity and model generalization bottlenecks in robotics. Through low-cost, scalable data collection combined with a proprietary data augmentation engine, they achieve significant improvements in data quality, richness, and scale. Each data asset is also traceable on-chain, forming a core fuel source for the evolution of general robot intelligence (RGI).

Axis innovates how training data is provided. Most existing projects gather data by mobilizing users to record videos of specified actions via smartphones or smart glasses, which is low-cost and accessible globally. However, such video data lacks physical realism, depth information, and cannot guarantee the continuity and accuracy of 3D data.

Axis solves this with “simulation,” creating diverse virtual scenarios (lighting, angles, friction, dynamics) that allow models to perform tasks under more challenging virtual conditions, greatly enhancing generalization. They adopt a Hybrid Strategy, combining scarce real data with large amounts of synthetic data. GPU-accelerated metadata augmentation introduces variations in lighting, textures, and physical properties within a scene. Virtual scenes are not static or hardcoded but flexible, generated via code to produce countless scenarios, enabling robots to face more rigorous and comprehensive challenges. This approach, validated by giants like Google and NVIDIA, reduces costs and increases output.

Their community-opened first simulation project, “Little Prince’s Rose,” has been completed. In this project, users control a robot to water a plant in a simulated environment via a web interface, collecting and analyzing user actions to teach the robot. This maintains low-cost, accessible data collection while building a native 3D-aware Vision-Language-Action (VLA) model, enhancing spatial reasoning.

Within five days of launch, thousands of non-expert users worldwide contributed high-quality trajectories for training. Using this data, Axis trained a policy model and successfully replicated it on a real Franka arm. This completes a full stack cycle: task generation → community collection → data augmentation → model training → real-world deployment.

One hour of real data can generate 1,000 hours of training data, greatly reducing the cost of robot model generalization.

During the Spring Festival beta test, in just five days, 18,000 participants without robotics backgrounds completed 27 new tasks, contributing over 100,000 data trajectories. The test supported high task randomization and proved compatibility with wheeled and dual-arm robots.

Axis’s core product will be officially released in late March, with plans to open-source the world’s largest pure simulation dataset based on Franka arms by late April or early May, fully supporting policy and model training needs. As a Crypto-AI-originated project, Axis is also exploring industry applications, collaborating with benchmark clients across sectors: automating production lines with a car manufacturer, partnering with a semi-IPO compute company on virtual assets and world models, and working with several embodied entity firms on virtual simulation data collection and model training. These demonstrate the external value of crypto projects.

GEODNET

A decentralized network providing centimeter-level real-time dynamic positioning data for drones and robots, with over 21,000 active stations across more than 150 countries. Last year, the project generated over $7 million in revenue, with quarter-over-quarter growth.

Although mainly categorized as DePIN, the increasing adoption of robotics in real life will expand the demand for high-precision real-time positioning data. In February 2025, Multicoin announced it led an $8 million acquisition of $GEDO tokens from the GEODNET Foundation.

BitRobot

BitRobot Network, developed jointly by FrodoBots Lab and Protocol Labs, aims to enable distributed robot work and collaboration. Its key components include: verifiable robot work (VRW), which quantifies and rewards robot tasks; device node tokens (ENT), representing device ownership and network access (NFT format); and subnets, resource clusters for task execution and value creation within the network.

On February 14, 2025, FrodoBots announced a $6 million seed round, totaling $8 million raised.

FrodoBots also sells robots, such as Earth Rovers, priced at $249. Players remotely control their robots via a browser in a global treasure hunt game called ET Fugi, with data used by researchers to test AI navigation models. ET Fugi is the first subnet of BitRobot.

Another upcoming robot game, Octo Arms, will let players remotely control robotic arms to complete 3D puzzles and competitions.

The “subnet” concept is abstract; simply put, any cluster contributing to the overall ecosystem (like ET Fugi or SeeSaw by Virtuals) is a subnet.

SeeSaw

BitRobot’s fifth subnet, launched in October last year by Virtuals, is a robot training data sharing app. Users upload videos of daily behaviors (e.g., tying shoes, folding clothes) to earn rewards. These videos from global users will be used to train robots.

Auki

Auki’s decentralized perception network, Posemesh, connects humans, devices, and AI. It’s built on a DePIN architecture, allowing robots, AR glasses, and other devices to share location and sensor data in real-time, co-constructing a collaborative spatial understanding of the physical world. This shared spatial view supports robots, AR, and AI.

Posemesh features multiple node roles: compute nodes provide processing power; motion nodes (robots) upload position and sensor data; reconstruction nodes generate 3D maps; domain name nodes manage 3D spaces. Nodes are rewarded with $AUKI tokens based on contribution, fostering an evolving machine vision network.

The network emphasizes privacy, preventing single entities from monitoring private spaces, and can be applied in retail (product placement), property management (asset tracking), event navigation, and construction.

Their Cactus AI spatial computing platform has been piloted with Toyota Material Handling and Swedish retailer Stora Coop.

XMAQUINA

A DAO enabling retail investors to participate in robot companies. It has raised $10 million through token sales of $DEUS. The DAO has invested in six robotics firms, including Apptronik, Figure AI, Agility Robotics, 1X Tech, NEURA Robotics, and Robotico, with some investments already profitable, even yielding over 100% returns on single deals.

PrismaX

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

PrismaX builds an open coordination layer connecting remote operators, robot users, and robot companies. Operators can remotely control robots, perform tasks, and collect valuable data, or request services like logistics and advertising.

It also offers a protocol for remote robot operation, allowing companies to find experienced operators for complex tasks. Operators can stake network tokens to boost trust and access high-value tasks. Rewards depend on staking amount, work quality, and efficiency, incentivizing high performance.

Data collected from remote operations trains robots, increasing their autonomy, which in turn improves operator efficiency, creating a virtuous cycle toward fully autonomous robots.

NRN Agents

NRN evolved from AI Agent battle games like AI Arena. On October 28, 2021, ArenaX Labs announced a $5 million seed round led by Paradigm Capital, with Framework Venture Partners participating. On January 9, 2024, they completed a $6 million Series A led by Framework Ventures, with SevenX Ventures, FunPlus/Xterio, and Moore Strategic Ventures also investing.

While still a data collection and reinforcement learning process, leveraging extensive gaming experience, NRN offers browser-based experiences that turn robot data collection into games. Users control simulated robots directly via browser, and their actions generate data used to train real-world robots.

Currently, the focus is on mechanical arms (RME-1) to validate data collection, real-time learning, and adaptation.