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What are the application scenarios of RobotFi?
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As artificial intelligence and robotics technologies are rapidly merging, humanoid robots are gradually moving from laboratories to the public, and domestic service has become one of the most promising scenarios for their implementation. With their anthropomorphic appearance, flexible joint movements, and increasingly sophisticated AI interaction systems, these robots can complete a variety of household tasks, including cleaning, preparing meals, accompanying the elderly, and taking care of children.
This is where RobotFi gets interesting. Instead of relying on centralized insurance companies or corporate guarantees, developers can develop off-chain services to bring robots onto the chain, and further develop supporting services for robots, such as housekeeping services. To ensure the safety and reliability of the service, developers can attract LPs on the chain to inject collateral, which will act as insurance and economic security. In return, LPs will receive actual returns generated by the service.
Smart contract controlled compensation mechanism: The insurance is managed by smart contracts. These smart contracts define the specific conditions that trigger the compensation. Possible triggering events include the failure of the robot strategy, which will trigger the LP's compensation, similar to the slashing penalty mechanism, to compensate users who purchased the robot strategy service. If the task is successfully completed without any abnormalities, the robot diagnosis system will report the task completion status to the off-chain service and issue the payment to the LP.
The tenant may also need to pass certain KYC verification to prevent him from running away with the robot, and the tenant's credit qualifications are likely to affect the developer's insurance premium costs. For example, if the tenant has a good on-chain reputation and/or has a high-income zero-knowledge proof verification, then the developer needs to pay a lower premium, and vice versa.
The insurance mechanism helps avoid major losses caused by robot failure, damage or liability accidents, reduces operational risks, and increases the willingness to own robots.
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