Embodied AI, Explained: When Robots Leave the Chatbox and Hit the Real World

What is Embodied AI?

Artificial intelligence is a common topic of discussion, but it has expanded way beyond what we know or think. In today's article, I will be discussing Embodied Artificial Intelligence(AI) and what it means for the Blockchain. Embodied AI integrates artificial intelligence agents into physical bodies like robots and vehicles, enabling them to interact sensorily with the physical world. Embodied AI creates systems that can interact with their immediate environment with the aid of technologies like sensors, motors, machine learning and natural language processing. Embodied AI is set up to be able to respond to different kinds of sensory input, like the way human beings respond to the five senses. However, the systems are better set up in the sense that they can detect other senses such as ultraviolet rays, magnetic field, infrared light, among some other things invisible to the human eye.

In the 1960’s, Stanford Research Institute developed a robot called Shakey. Shakey was the first documented robot to perceive it's surroundings and figure out how to solve problems without human intervention. The goal of embodied AI is giving an artificial intelligence agent a physical body to improve by learning and solving problems. Embodied AI systems learn not just from gathered human data, but from physical trial and error, as opposed to regular AI. For instance, a robot trained to vacuum houses would learn how to dodge the bumps from time to time. Embodied AI also utilises multimodal learning to understand environments, i.e feeling and touching. Embodied AI has an edge over regular AI in the sense that it uses sound, touch, sight and movement to understand patterns that software based AI cannot fully understand. Embodied AI is useful in several applications from vacuum robots to self-driving cars, changing the way we interact with technology.

How Does Embodied AI Work?

Embodied AI works by integrating intelligent algorithms into physical bodies like robots, drones and autonomous vehicles. These algorithms then perceive, navigate and interact with the real world in real time. The systems learn from experience, adapt to different environments and carry out tasks through direct interaction, rather than just processing data. Embodied AI integrates not just artificial intelligence and robotics, but has come to include other fields such as computer vision, environment modelling, prediction, planning, control, reinforcement learning, and physics-based simulation.

In combining these domains, Embodied AI systems improve their behaviour from experience, enabling them to adapt and respond effectively to real world challenges. There are some main core principles of Embodied AI and they include:

Interaction with the physical world: Embodied AI agents interact with the real world, helping them gather real time data and adapt to changing conditions. A robot must learn how to handle objects and carry out tasks by interacting with others.
Perception and action coupling: In embodied AI systems, perception and action are interconnected. This allows for more adaptive and responsive behaviours in real time.
Learning through experience: Embodied AI utilises past experiences to make informed decisions. These past experiences may provide feedback that will lead to better performance over time, e.g a dancing robot.
Contextual understanding: Embodied AI systems make decisions based off information on their surroundings e.g a robot vacuum cleaner understands the corners and crevices of your house
Multimodal sensory integration: Embodied AI systems use multiple sensory systems to perceive and interact with the environment e.g a robot using sight and touch to handle objects

Embodied AI and the Blockchain: The Interconnection

Embodied AI merges with the Blockchain quite beautifully: the concept of decentralised embodied AI systems that can use physical devices to carry out on chain tasks is mind-blowing. For example, Open Mind, a pioneer in decentralised robot operating systems has partnered with Circle, the issuer of the USDC Blockchain. The partnership was aimed at creating a foundational payment platform for Embodied AI, enabling intelligence machines to carry out transactions seamlessly. Imagine a robot being able to utilise USDC to make purchases on chain? It would become a great example of true machine to machine (M2M) commerce, enabling robots to carry out commercial activities without human interference. This will create better efficient systems, and with the added benefit of decentralisation, create a transparent platform where fraudulent activity is almost impossible. This creates an innovative business platform, and encourages key players in artificial intelligence to explore newer solutions.

In April 2025, Michael Cho the co-founder of FrodoBots Labs, Juan Benet the founder of Protocol Labs; and Jonathan Victor the co-founder of Ansa Research discussed the promise of their new project, BitRobot. BitRobot is a decentralised AI research network designed to push the boundaries of embodied AI. The concept involves utilizing cryptocurrency DePIN to create a large scale robotics network that can solve the grand challenge of generating data required to train models for embodied AI. BitRobot is a network of subnets: in the sense that it is flexible and expansive. One subnet could take care of one type of robot, while another subnet may involve a human gamer and yet another one would just be humanoid. The human gamer subnet could run with DePIN and the deployment of robots. The output of that particular subnet would be real data sets. When you want to set the project to scale, your required contributors would be compute researchers and providers. Each subnet has a defined output and each is like a mini ecosystem to contribute to the advancement of embodied AI. This goes to show how well the Blockchain and embodied AI are interacting for innovative projects.

Embodied AI on-chain: Use Cases

There are different applications, for the use of Embodied AI on the Blockchain. They include:

Autonomous robotics and fleet management: The Blockchain provides a platform for fleets of autonomous agents, allowing them to carry out their activities without a single point of failure. Decentralised autonomous robots carry out tasks together and they share updates on a Blockchain network, creating more efficient systems.
Smart logistics and supply chain: Internet of things (IoT) sensors and automated warehouse robots use the Blockchain for “Chain of Custody” and real time operational trust. Embodied AI drones and crawling robots can scan inventories and report their findings back to the blockchain.
Smart cities and infrastructure: Embodied AI systems interact with decentralised ledgers to manage resources and ensure public safety. AI embodied drones can enforce congestion pricing or identify parking violators, recording these actions on the Blockchain to ensure transparency and prevent corruption.
Decentralised AI marketplace and Research(DeSci): Embodied AI models can be trained, verified and monetised without central interference on the Blockchain. In healthcare, multiple hospitals can train a shared AI model for diagnostic robots. A robot can use Zero knowledge proofs(ZKPs) to prove it made a decision based on a regulatory approved model without revealing it's private training data.
Intelligent prosthetics and rehabilitatative robotics: The wearable embodied AI systems uses blockchain to protect the sensitive behavioural data that they collect. AI powered prosthetic limbs can share movement patterns across a decentralised network. These limbs can acquire better knowledge on mobility and send updates on the blockchain. These updates can be used to train more systems, without revealing the individual user's private data.
Specialised cooking robots: In the near future, it is likely that home robots that cook meals will breakout in our kitchens. The robots will use the Blockchain for recipe licensing and ingredient provenance. A good example is Posha. The Blockchain allows chefs to monetize their recipes as intellectual property that robotic kitchens can use.
AI agent to agent protocols(A2A protocols): Standardized communication protocols will allow different robots and digital assistants to negotiate tasks and safety boundaries across different blockchain networks. A2A protocols enable autonomous agents to interact, trade and coordinate without human contact. With an A2A protocol, two autonomous agents can carry out transactions with set rules that ensure seamlessness.

Embodied AI on-chain: Possible Disadvantages

Integrating Embodied AI with the Blockchain has some disadvantages. These disadvantages are often as a result of the friction between the high speed, data intensive nature of the robots and the slow, immutable decentralised nature of the Blockchain.

Security Risks: AI in itself, is highly susceptible to external manipulation and data attacks. Adversaries can exploit embodied AI models by tailoring prompts based on the data that was used during training. If an attacker understands the methodology, they can engineer specific prompts to describe the model, utilising it to their advantage.
Privacy and transparency: Blockchain transparency is one strong characteristic that fosters trust and accountability in the ecosystem. This transparency is often not in line with the requirements of most AI systems: they may rely on large datasets that may contain confidential client data e.g in healthcare. Any breach in these data could result in legal violations and ethical concerns.
Physical harm: Automated systems in different sectors such as healthcare and manufacturing may put human beings at the risk of physical harm. AI controlled robots can from misinterpreted behaviour or malfunction accidentally cause harm to individuals. A humanoid robot may not be able to understand that a kettle of boiling water may be dangerous when spilled, and may even get into dangerous situations by itself.
Labour displacement: There are arguments about embodied AI and even virtual AI taking over human job roles and leaving many people unemployed. Classical industrial robots have already taken over human roles in manufacturing and robot deployment will definitely lead to a reduction in human deployment. Future technological advancements point towards seeing Embodied AI agents carry out human tasks without needing sleep, breaks or vacations.
Misinformation: Non embodied AI systems have been known to spread false information. Embodied AI systems inherit that shortcoming as they can easily answer user questions with deceptive or false information. Their sensory abilities even means that their hallucinations can be spatially grounded. The most dangerous part of it all is that the model developers and malicious attackers can utilize them as tools for propaganda.
Technical and Operational limitations: Blockchain systems often run on CPUs while AI requires powerful GPUs. Creating GPUs that work with Blockchain consensus protocols is not an easy task. In addition, both blockchain validation and AI training are expensive and power consuming. The cost of developing hardware and software for embodied AI and running it on the Blockchain is both expensive and complex.
High Latency and Poor scalability: Embodied AI needs instant decision making to operate safely in dynamic, physical environments. Blockchain networks often experience congestion and latency, making them unsuitable for real time control. In addition, the amount of data generated by sensors cannot be effectively stored or processed directly on a Blockchain.

Embodied AI Market and User Adoption

The Embodied AI market is projected to reach 23.08 billion in 2030 from 4.44 billion in 2025, growing at a CAGR of 39%. The embodied AI market is experiencing rapid growth that is driven by advancements in robotics, machine learning and sensor technologies that enhance the capabilities of autonomous systems across multiple industries. This turnaround is notable in sectors like healthcare where robotic assistance and diagnostics are revolutionising care, and in logistics where there is the development of autonomous delivery systems. The embodied AI market is changing and these changes are driven largely by customer needs. Traditional revenue sources such as industrial robots, fixed function automation systems and rule based navigation are being replaced by next generation platforms such as autonomous mobile robots (AMRs), humanoid robots, AI enabled exoskeletons and collaborative robots. Trends such as generative AI, vision language models and multimodal AI for human robot interactions (HRI) are changing the way these machines work.

Adoption of embodied AI is driven by the advancement in these trends and increased need for automation in unstructured environments. For example, in the logistics sector, there is a high demand for autonomous mobile robots (AMRs) and AI enabled sorting systems to handle e-commerce volume. Organisations like Amazon Robotics and Locus Robotics are utilizing embodied AI for that purpose. In industrial manufacturing, about 31% of the market share was focused on manufacturing, especially collaborative robots(cobots) in 2024.

In the field of healthcare, user adoption is rising for surgical robots, rehabilitation devices and service robots e.g ElliQ designed for assistance with the elderly. There are different factors that drive user adoption and they include cost, safety, ethics and workforce integration. Users are looking for cheaper, safe, ethically approved embodied AI agents that can handle routine hazardous tasks, while human beings handle complex tasks. The future will likely see a shift from "intermediate" to "advanced" embodiment (Level 3), where AI systems demonstrate deeper situational awareness and complex reasoning.

Embodied AI: The Rise of Physical AI on the Blockchain

Embodied AI is more than just AI, it is more than a physical AI solution that is taking over the blockchain. In 2025, GAIB, the first economic layer for AI and compute announced its entry into the robotics sector. GAIB promised to build on it's expertise of financing GPU infrastructure on-chain and drive the tokenization of embodied AI. The plan was simple: physical robots and their future cash flows would be digitised and embedded into GAIB’s synthetic dollar, AID, allowing investors to capture yields from robots alongside GPUs. The tokenized assets gain liquidity across DeFi, where they can be used for lending, trading, and structured products. At the same time, financing for data infrastructure and supply chains accelerates robotics development cycles. In GAIB’s model, every humanoid, drone, or intelligent vehicle becomes part of a broader, decentralized onchain economy. GAIB is one example: embodied AI is one revolutionary innovation being adopted by more organisations on the chain.

Tesla in 2021, announced a humanoid robot called Optimus. Optimus is equipped to manipulate objects, and navigate complex environments. Tesla has already deployed early versions of Optimus in its factories, where the robot autonomously performs tasks such as handling battery cells and navigating obstacles. With plans to begin limited production in 2025 and scale to over 1,000 units for internal use by 2026, Optimus is poised to transform not only Tesla’s operations but also industries like logistics, healthcare, and even personal assistance. Organisations are already using Decentralised Physical Infrastructure Networks(DePIN) to crowdsource real world training data. Frodobots allows gamers to remotely control real world robots, generating demonstration data and earning token rewards. FF AI robotics partnered with AIxCrypto to explore Web 3 infrastructure for humanoid and quadrupled robots, focusing on decentralised data networks and “Brain” platforms. It is no wonder that multiple industries are jumping on the Embodied AI on chain trend: it is indicative of the many possibilities in tech.

References