DePIN and Embodied Intelligence: Technical Challenges and Future Outlook

Recently, a discussion on "building decentralized physical artificial intelligence" has sparked industry attention. Michael Cho, co-founder of FrodoBot Lab, shared the challenges and opportunities faced by decentralized physical infrastructure networks (DePIN) in the field of robotics. Although this field is still in its infancy, its potential is enormous and could fundamentally change the way AI robots operate in the real world. However, unlike traditional AI that relies on large amounts of internet data, DePIN robot AI technology faces more complex issues, including data collection, hardware limitations, evaluation bottlenecks, and the sustainability of economic models.

This article will delve into the key issues faced by DePIN robotic technology, analyze the main obstacles to scaling decentralized robots, and discuss the advantages of DePIN compared to centralized methods. Finally, we will also explore the future development prospects of DePIN robotic technology.

DePIN Smart Robot's Bottleneck

Bottleneck 1: Data

Unlike "online" AI large models that rely heavily on training from internet data, embodied AI needs to interact with the real world in order to develop intelligence. Currently, there is no large-scale infrastructure established globally to collect such data, and there is no consensus in the industry on how to collect this data. The data collection for embodied AI is mainly divided into three categories:

1. Human-operated data: high quality, capable of capturing video streams and action labels, but high cost and labor-intensive.
2. Synthetic Data (Simulated Data): Suitable for training robots to move in complex terrains, but difficult to simulate the ever-changing task scenarios.
3. Video Learning: Learning through observing real-world videos, but lacking direct physical interaction feedback.

Bottleneck Two: Level of Autonomy

Achieving a high level of autonomy is a huge challenge. Take last-mile delivery as an example; a 90% success rate may seem good in a lab environment, but it is unacceptable in real life. To make robotic technology truly practical, the success rate needs to be close to 99.99% or even higher. However, each 0.001% increase in accuracy requires exponentially more time and effort.

Bottleneck Three: Hardware Limitations

Even with advanced AI models, existing robotic hardware struggles to achieve true autonomy. The main issues include:

• Lack of highly sensitive tactile sensors
• Difficulties in identifying obstructions
• The actuator design is not user-friendly, resulting in stiff movements and potential dangers.

Bottleneck Four: Difficulty in Hardware Expansion

The implementation of intelligent robot technology requires the deployment of physical devices in the real world, which poses significant capital challenges. Currently, the cost of efficient humanoid robots can reach tens of thousands of dollars, making large-scale adoption difficult.

Bottleneck Five: Assessing Effectiveness

Assessing physical AI requires long-term real-world deployment, which is a time-consuming and complex process. Unlike online AI models that can be tested quickly, the validation of robotic intelligence technology necessitates large-scale, long-term real-time deployment.

Bottleneck Six: Human Resources

The development of AI for robots still requires a substantial amount of human input, including operators providing training data, maintenance teams keeping the robots operational, and researchers continuously optimizing AI models. This ongoing human intervention is a major challenge that DePIN must address.

Future Outlook: Breakthroughs in Robotics Technology

Although the widespread adoption of general-purpose robotic AI is still some distance away, the progress in DePIN robotic technology gives us hope. The scale and coordination of decentralized networks can distribute the capital burden and accelerate the processes of data collection and evaluation.

The following aspects demonstrate the potential of DePIN in promoting the development of robotics technology:

1. Accelerate data collection and evaluation: Decentralized networks can run in parallel, collect data, and improve efficiency.

2. AI-driven hardware design improvements: Utilizing AI to optimize chip and materials engineering could significantly reduce development time.

3. Decentralized computing infrastructure: Enabling global researchers to train and evaluate models without capital constraints.

4. New Profit Model: For example, the autonomous operation model demonstrated by AI agents, which maintains finances through decentralized ownership and token incentives.

Summary

The development of AI robots involves multiple aspects such as algorithms, hardware upgrades, data accumulation, funding support, and human participation. The establishment of the DePIN robot network means that the power of decentralized networks can be leveraged to collaboratively collect robot data, share computing resources, and invest capital on a global scale. This not only accelerates AI training and hardware optimization but also lowers the barriers to development, allowing more researchers, entrepreneurs, and individual users to participate.

In the future, we expect the robotics industry to no longer rely on a few tech giants, but to be jointly driven by the global community, moving towards a truly open and sustainable technology ecosystem. The development of DePIN may become a key force in driving breakthroughs in robotics technology, paving the way for smarter and more widespread robotic applications.