Decentralized Finance能帮助我们筛选最优的 Bots 服务方案

Source: Cointelegraph Original text: "DeFi can help us filter the best robot service solutions"

The opinion comes from: OpenMind researcher Paige Xu

As global teams accelerate the deployment of humanoid robots in the fields of healthcare, manufacturing, and defense, how to select the optimal robot for specific tasks has become a core challenge in robotics. Whether it's drones delivering medical supplies, robots surveying hazardous locations, or AI agents responding to cyber threats, the task allocation scheme for human-robot collaboration directly determines the success or failure of the mission—incorrect choices not only waste resources but are also more likely to lead to catastrophic consequences in high-risk environments.

To build an efficient human-machine hybrid team, it is essential to accurately understand task attributes, environmental characteristics, and collaboration modes. Decentralized Finance (DeFi) provides innovative solutions for this: its core principles (decentralization, transparency, automation) lay the foundation for constructing smarter human-machine collaboration systems. Through tools such as auction mechanisms, bidding systems, and reputation systems, we can establish a fairer task allocation framework that alleviates labor shortages in key industries while achieving seamless collaboration.

Competition drives efficiency

The task allocation of robotic systems has inherent complexity, involving multiple intelligent agents with varying abilities, costs, and resource needs. Traditional centralized allocation models are difficult to scale across enterprises and borders and carry the risk of single points of failure.

The bidding mechanism provides a market-driven solution. In this model, tasks become the "resources" that agents bid for, with allocation based on quantifiable metrics such as cost, timeliness, and quality. The most common types include reverse auctions (where service providers bid the lowest price) and Maximal Extractable Value (MEV) auctions. MEV auctions allow "searchers" to bid for transaction packaging priority by paying a portion of their earnings to validating nodes, usually adopting a second-price sealed-bid auction format (the highest bidder wins but pays the second-highest price), ensuring fairness while incentivizing honest bidding.

Flashbots further introduces a private bidding layer, significantly enhancing network efficiency and alleviating congestion by optimizing the management of scarce resources such as block space. This model based on competition and self-optimization is analogous to the mechanism of DeFi platforms optimizing liquidity through auctions.

A New Paradigm for Robot Collaboration

In intelligent machine systems, the auction logic is reversed: machines bid for tasks by providing optimal service solutions (rather than paying for them), which is known as reverse bidding. When a task is issued, qualified agents will assess their execution capabilities and submit bids based on cost, time, and quality. The system allocates tasks based on the optimal combination of efficiency, speed, and reliability—this differs from the "highest bidder wins" logic in MEV auctions, placing greater emphasis on cost-effectiveness and performance orientation.

Dynamic Team Collaboration

Complex tasks often require human-machine teamwork to complete. For example, in firefighting missions, drones are responsible for aerial reconnaissance, firefighters operate water guns, and ground robots ensure material supply. In such scenarios, humans and machines can submit joint bids through dynamic team formation. The winning team uses a decentralized communication system to share information and coordinate actions in real time, and the complexity of their collaboration and efficiency improvement logic is similar to MEV auctions, but has been customized to meet the needs of robotic systems.

Similar to human teams, incentive mechanisms are also crucial: successfully completing tasks can earn reputation points or token rewards, increasing the probability of winning future bids, thereby creating a positive cycle that drives continuous improvement.

The transformative potential of the bidding mechanism

The bidding model provides urgently needed decentralized solutions for robotics, freeing itself from reliance on centralized task allocation systems, enabling agents to self-organize and collaborate dynamically. This mechanism, which integrates competition, transparency, and adaptability, opens up new paths for scalable decentralized collaboration.

Its similarity to DeFi is astonishing: just as MEV auctions optimize block space utilization, reverse bidding ensures tasks are handled by the most cost-effective agents, further enabling multi-agent collaboration, real-time adaptation, and reputation-based continuous evolution. By introducing DeFi's economic model into the robotics ecosystem, we are building a future of seamless human-machine collaboration—these decentralized, trustless systems are centered around efficiency, fairness, and innovation, ushering in a new era of collaboration.

The essence of DeFi is to break down financial barriers, promote the free flow of capital, and optimize resource allocation. These principles naturally align with autonomous agents in decentralized ecosystems. This is just the beginning of the on-chain economy: humans and machines will work together to execute payments, process tasks, and establish a more transparent and efficient collaborative network—this is the convergence point of cryptocurrency and general artificial intelligence.

The viewpoint comes from: OpenMind researcher Paige Xu

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This article is for informational purposes only and does not constitute any legal or investment advice. The views expressed herein are solely those of the author and do not necessarily reflect the position of Cointelegraph.