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

The decentralized physical infrastructure network (DePIN) is facing significant opportunities and challenges in the field of robotics. Although this area is still in its early stages, its potential should not be underestimated, as it is expected to fundamentally change the way AI robots operate in the real world. However, unlike traditional AI that relies on massive 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 delve into the main obstacles faced by DePIN robotic technology, analyze the advantages of decentralized methods compared to centralized solutions, and look forward to the future development prospects of DePIN robotic technology.

Key Bottlenecks of DePIN Smart Robots

Data Collection Challenge

Embodied AI needs to interact with the real world to develop intelligence, which is fundamentally different from "online" AI large models that rely on internet data. Currently, there is still no infrastructure established globally to support the large-scale development of embodied AI, and the industry has not reached a consensus on how to effectively collect such data. The data collection for embodied AI can be mainly divided into three categories:

1. Human-operated data: High quality, but expensive and labor-intensive.
2. Synthetic Data (Simulated Data): Suitable for specific fields, but difficult to simulate complex and changing real-world scenarios.
3. Video learning: Huge potential, but lacks direct physical interaction feedback.

Improvement of autonomy level

To achieve commercial applications of robotic technology, its success rate needs to be close to 99.99% or even higher. However, every increase of 0.001% in accuracy requires exponential time and effort. The progress of robotic technology is exponential in nature; with each step forward, the difficulty increases significantly. Achieving the final 1% accuracy may require years or even decades of effort.

Hardware Limitations

Even with advanced AI models, the existing robotic hardware is not yet ready to achieve true autonomy. The main issues include:

• Lack of tactile sensors
• Difficulty in object occlusion recognition
• The actuator design is not biological enough.

These hardware limitations severely affect the robot's perception ability and action flexibility.

The Dilemma of Hardware Expansion

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

The challenge of assessing effectiveness

Unlike online AI large models that can be tested quickly, evaluating physical AI requires long-term and large-scale deployment in the real world. This process is time-consuming and costly, and the only verification method is to observe its failures.

Human Resource Demand

In the development of robot AI, human labor is still indispensable. Robots require human operators to provide training data, maintenance teams to ensure operation, and researchers to continuously optimize AI models. This ongoing human intervention is one of the main challenges that DePIN must address.

Future Outlook: Breakthroughs in DePIN Robotics Technology

Although the large-scale application of general-purpose robotic AI still takes time, the progress of DePIN robotic technology brings hope to the industry. The scale and coordination of decentralized networks help to disperse capital burdens and accelerate the data collection and evaluation process.

1. Accelerated Data Collection and Assessment: Decentralized networks can run in parallel and collect data, greatly improving efficiency.

2. AI-driven hardware design improvements: Utilizing AI to optimize chip and materials engineering may significantly shorten the technology development timeline.

3. Decentralized Computing Infrastructure: Through DePIN, global researchers can access necessary computing resources without capital constraints to train and evaluate models.

4. Innovative Profit Model: Self-operating AI agents demonstrate how DePIN-driven smart robots maintain their finances through decentralized ownership and token incentives.

Conclusion

The development of AI in robotics not only relies on algorithm advancements but also involves hardware upgrades, data accumulation, funding support, and human participation. The establishment of the DePIN robot network brings new possibilities to the industry by accelerating AI training and hardware optimization through global collaboration, lowering development barriers. This model is expected to help the robotics industry break free from dependence on a few tech giants and form an open, sustainable technology ecosystem driven by a global community.