The core of DeFi lies in automating financial processes through code, while robots are dedicated to automating physical tasks. The combination of the two is a natural extension of automation development. If we believe in the power of programmable money, smart contracts, and artificial intelligence, then expanding this programmability to robots—that is, physical programmable AI agents—would be the next logical evolution.
One of the strongest leaders in the robotics field today is Unitree Technology.
Although robots like those from Unitree Technology are still many years away from mainstream applications, and the idea of putting robot data on the blockchain sounds more like a distant fantasy, it doesn’t stop us from daydreaming.
How Can RobotFi Be Implemented Today?
Today’s robots are not directly connected to the blockchain at the hardware level. They do not have built-in blockchain nodes or cryptographic processors (an interesting idea to explore later).
Therefore, to bring existing robots onto the chain, we need a bridging layer or intermediary layer (usually an off-chain service or server) to connect robots and the blockchain. Each robot also needs to be assigned a dedicated wallet address.
Unitree robots can use their existing communication capabilities (such as Wi-Fi, Ethernet, and possibly cellular networks) to connect to off-chain services via standard network protocols (e.g., HTTP, WebSocket, etc.). Subsequently, these off-chain services will interact with the blockchain using standard blockchain libraries and APIs (such as Web3.js, Ethers.js).
Smart contracts on the blockchain can trigger Unitree robots to perform operations through off-chain services. For example, when the off-chain service detects a payment completed to the address associated with the robot, it sends instructions to the robot to execute a specific task.
I also envision that future robots can be programmed like smart contracts, capable of executing various "action scripts or robot strategies." These strategies can be created by independent developers, thus allowing robots to be seen as physical smart contracts or AI agents.
Initially, the scripts created would be in a "wild west" state, where you could program robots to perform a variety of operations, except for certain prohibited actions. There would be an independent security or management system for real-time monitoring to prevent robots from engaging in any dangerous behavior. Once again, we are still dreaming.
This would allow robotics companies to focus on the robots themselves, rather than robot services. Robot services would be "outsourced" to developers to implement. The on-chain robot services running through off-chain services would be called RobotFi.
In other words, RobotFi would be a vertical track where participants can earn on-chain rewards by funding or developing robot-related activities.
What Are the Use Cases for RobotFi?
Overcollateralized Domestic Rental Services
One of the most popular applications for humanoid robots is domestic services.
Initially operated robot services may bring many risks.
Robots may malfunction, make mistakes, suffer damage, or fail to achieve the desired results. Traditional leasing and service models rely on trust in the platform or service provider.
This is where RobotFi gets interesting.
Developers can no longer rely on centralized insurance companies or corporate guarantees, but instead develop off-chain services to bring robots onto the chain and further develop supporting services for robots (such as domestic services). To ensure the safety and reliability of the service, developers can attract on-chain LPs to inject collateral, which will act as insurance and economic security. In return, LPs will receive the actual income generated by the service.
Mechanism Analysis:
Robot Strategy Insurance Pool: LPs deposit collateral assets into the pool to provide risk coverage for robot strategies, and in return, they receive the income generated by the strategy.
Robot Strategy Insured Party: Strategy creators can purchase risk coverage for their robot strategies from the insurance pool. The specific premium depends on factors such as the type of robot and asset size, the risk coefficient of the task being performed, and the selected coverage amount.
Smart Contract-Controlled Payout Mechanism: The insurance is managed by smart contracts. These smart contracts define the specific conditions that trigger payouts. Possible triggering events include robot strategy failure, which would trigger payouts from LPs (similar to a slashing mechanism) to compensate users who have purchased the robot strategy service. If the task is completed successfully without any incidents, the robot diagnostic system will report the task completion status to the off-chain service and release payment to the LPs.
In the above example, although I have described robots and robot strategies separately, the operation mechanism would be equally effective if the robot and robot strategy were combined into a single leasing project. In this case, the security coverage could extend to the robot itself. For example, if the robot itself is damaged during the lease, the relevant compensation would be paid to the robot owner.
Tenants may also need to go through some KYC verification (to prevent them from running away with the robot), and the tenant's creditworthiness is also likely to affect the developer's insurance cost. For example, if the tenant has a good on-chain reputation and/or high income (zero-knowledge proof verification), the premium that the developer needs to pay will be lower, and vice versa.
Summarizing by Analogy to Blockchain:
Robots (Infrastructure/Chain): Provide the core infrastructure, that is, a robot that is easy to program and has high physical performance.
Robot Services (On-Chain Applications): Specific tasks programmed by experts, just like applications built on top of the robot infrastructure.
Robot Insurance (Collateral Provided by LPs): The collateral of LPs acts as the security and economic guarantee for robot services. They provide trust, security, and operational mechanisms for risk and failure handling in the RobotFi ecosystem, just as collateral in the Automated Verification System (AVS) is used to provide security for on-chain transactions and network operations.
Strictly speaking, you are not required to purchase insurance. Although there are certain advantages to obtaining robot services through on-chain payments, these advantages are not significant. Since robots are in the real physical world, purchasing insurance can effectively enhance consumer trust and acceptance. In comparison, uninsured services are unlikely to gain the same level of user recognition.
Economic Alignment and Incentives for Good Robot Behavior
This insurance/collateral mechanism system creates strong economic incentives for good robot behavior and responsible strategies, benefiting all participants:
LP Incentives:
Premium Income: LPs earn income from the premiums paid by robot owners. This income must be attractive enough to incentivize them to lock their funds in the insurance pool.
Risk-Adjusted Returns: Differentiated insurance pools can be established for different risk levels (robot types/task categories). High-risk pools compensate for the risk of payouts with higher returns, allowing LPs to choose their risk-return preferences.
Robot Owner/Strategist Incentives:
Reduced Financial Risk: The insurance mechanism helps mitigate significant losses caused by robot malfunctions, destruction, or liability accidents, reducing operational risks and increasing the willingness to hold robots.
Competitive Advantage: Robot owners who offer insured services can differentiate themselves in the market, gaining higher rental premiums by building user trust.
Robot Manufacturer/Developer Incentives:
Reliability Demand: The insurance system indirectly pushes manufacturers to improve product reliability. Robots with low failure rates and good safety records will enjoy lower premiums, enhancing market competitiveness.
Data-Driven Iteration: Insurance claim data (types of failures/causes of damage) provide manufacturers with insights to improve product design, driving technological optimization.
User/Tenant Incentives:
Trust Building and Risk Mitigation: The insured mechanism enhances users' confidence in RobotFi services, providing financial protection against economic losses caused by robot failures when renting robots.
Access to High-End Equipment: The insurance mechanism reduces the economic risk of leasing high-value robots, encouraging more advanced devices to enter the rental market.
Fair Compensation Mechanism: When robots fail or tasks are not executed successfully, users can receive compensation through insurance payouts, optimizing the service experience.
Challenges Facing RobotFi
Although the concept of RobotFi is interesting, there are many challenges, and we are far from being ready. The main challenges are concentrated in two core areas: the centralization/data verifiability mechanism in the robotics field and the quantification assessment system for insurance payouts.
Off-Chain Service Dependency: As we discussed earlier, off-chain services are almost inevitable under current technological conditions. These services become the centralized control nodes and potential points of failure in the system. Whoever controls this service will have significant influence over the RobotFi system.
Reliability and Verifiable Data for Insurance Payouts: Insurance payouts rely on verifiable evidence of robot malfunctions, damage, or task execution failures. How to reliably and trustlessly transfer this data from the physical world to the on-chain system is an extremely complex challenge.
Fair Claims Assessment: In a decentralized RobotFi context, how to determine whether a claim is valid and how much the payout should be? Traditional centralized insurance companies rely on claims adjusters, but how can a decentralized system achieve this?
Final Thoughts
This is not a serious article about RobotFi but a potential vision. Although the concept of RobotFi is interesting, its feasibility depends on overcoming many significant technical, economic, and centralization challenges.
What is currently unclear is whether, compared to concentrating the entire robot ecosystem in the hands of a few key companies that predefine the functions of robots, the concept of RobotFi has enough advantages.
