The Integration of DePIN and Embodied Intelligence: Technical Challenges and Future Prospects

Decentralized Physical Infrastructure Networks (DePIN) are facing challenges and opportunities in the field of robotics. Although this domain is still in its infancy, it has the potential to fundamentally change the way AI robots operate in the real world. However, unlike traditional AI that relies on vast amounts of internet data, DePIN robotic AI technology faces more complex issues, including data collection, hardware limitations, evaluation bottlenecks, and the sustainability of economic models.

This article will explore the main issues faced by DePIN robotics technology, expand on the key obstacles to decentralized robotics, and discuss the advantages of DePIN compared to centralized approaches. Finally, we will examine the future development prospects of DePIN robotics technology.

The bottleneck of DePIN smart robots

Bottleneck 1: Data

Embodied AI needs to interact with the real world to develop intelligence, but currently there is a lack of large-scale infrastructure to collect this data. Data collection can be divided into three categories:

1. Human-operated data: High quality, capable of capturing video streams and action labels, but costly and labor-intensive.

2. Synthetic Data (Simulated Data): Suitable for training robots to navigate in complex terrains, but performs poorly on tasks that are highly variable.

3. Video Learning: Learning through observing real-world videos, but lacking direct physical interaction feedback.

Bottleneck 2: Level of Autonomy

For robotics technology to be truly practical, the success rate needs to approach 99.99% or even higher. However, the difficulty of improving accuracy grows exponentially, and the last 1% of accuracy may take years or even decades to achieve.

Bottleneck Three: Hardware Limitations

The existing robot hardware is not yet ready to achieve true autonomy. The main issues include:

• Lack of highly sensitive tactile sensors
• Obstacle recognition is difficult
• The actuator design is not biological enough, resulting in stiff and inflexible movements.

Bottleneck Four: Difficulty in Hardware Expansion

The technology of intelligent robots needs to deploy physical devices in the real world, which brings enormous capital challenges. Currently, the cost of humanoid robots is still high, making large-scale adoption difficult.

Bottleneck Five: Assessing Effectiveness

Evaluating physical AI requires long-term real-world deployment, which demands significant time and resources. The only way to validate robotic intelligence technology is to observe its failures, meaning large-scale, prolonged real-time deployment is necessary.

Bottleneck Six: Human Resource Demand

The development of AI for robots still requires a large amount of human involvement, including operators providing training data, maintenance teams keeping the robots running, and researchers continuously optimizing the AI models.

Future: When will breakthroughs in robotics arrive?

Although general-purpose robotic AI is still some distance from widespread adoption, the progress of DePIN robotic technology offers hope. The scale and coordination of decentralized networks can spread the capital burden and accelerate the data collection and evaluation process.

Some positive developments include:

1. Research institutions are collecting unique real-world robot interaction data through practical competitions.

2. AI-driven hardware design improvements may accelerate the development process.

3. Decentralized computing infrastructure enables global researchers to more easily train and evaluate models.

4. New profit models are emerging, such as autonomous AI agents maintaining finances through token incentives.

Summary

The development of AI in robotics involves multiple aspects such as algorithms, hardware upgrades, data accumulation, funding support, and human participation. The establishment of the DePIN robot network is expected to accelerate AI training and hardware optimization, lowering the development threshold and allowing more participants to join this field. In the future, the robotics industry may no longer rely on a few tech giants, but instead be driven by a global community, moving towards a truly open and sustainable technological ecosystem.