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

Recently, a discussion on "Building Decentralized Physical Artificial Intelligence" has attracted widespread attention in the industry. Experts at the conference delved into the challenges and opportunities faced by decentralized physical infrastructure networks (DePIN) in the field of robotics. Although this area is still in its infancy, its potential is enormous and is expected to fundamentally change the way AI robots operate in the real world. However, unlike traditional AI that relies on massive internet data, DePIN robotic AI technology faces more complex issues, such as data collection, hardware limitations, evaluation bottlenecks, and the sustainability of economic models.

This article will analyze the key points of the discussion, explore the challenges faced by DePIN robotic technology, expand on the main obstacles to 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.

Bottlenecks of DePIN Smart Robots

Bottleneck 1: Data

Unlike the "online" AI large models that rely heavily on vast amounts of internet data for training, embodied AI needs to interact with the real world to develop intelligence. Currently, there is no large-scale infrastructure established globally for this, and the industry has yet to reach a consensus on how to collect this data. The data collection for embodied AI can be divided into three main 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 variable tasks.
3. Video Learning: Learning through observing real-world videos, but lacking genuine physical interaction feedback.

Bottleneck 2: Level of Autonomy

For robotics technology to be truly practical, the success rate needs to be close to 99.99% or even higher. However, every increase of 0.001% in accuracy requires an exponential amount of time and effort. The advancement of robotics technology is not linear, but exponential in nature—each step forward significantly increases the difficulty.

Bottleneck Three: 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 highly sensitive tactile sensors
• Difficulty in identifying obstructions
• The actuator design is not bio-inspired enough, 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 presents significant capital challenges. Currently, only financially strong large companies can afford large-scale experiments.

Bottleneck Five: Assessing Effectiveness

Evaluating physical AI requires long-term real-world deployment, which is different from online AI large models that can be tested quickly. The only way to verify robotic intelligence technology is to observe its performance in long-term large-scale deployment.

Bottleneck Six: Human Resources

In the development of AI for robots, human labor is still indispensable. Human operators are needed to provide training data, maintenance teams keep the robots running, and researchers continuously optimize the AI models.

Future Prospects of Robotics Technology

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

AI-driven hardware design improvements, such as AI-optimized chips and materials engineering, could significantly shorten development timelines. Through DePIN decentralized computing infrastructure, researchers worldwide can train and evaluate models without capital constraints.

In addition, the new AI agents demonstrate innovative profit models of decentralized robotic technology networks. These AI agents can sustain their finances through decentralized ownership and token incentives, creating an economic cycle that benefits AI development and DePIN participants.

Conclusion

The development of AI in robotics depends not only on algorithms but also on hardware upgrades, data accumulation, financial support, and human participation. The establishment of the DePIN robotic network means that, with the power of decentralized networks, the development of robotic technology can be collaboratively carried out on a global scale, accelerating AI training and hardware optimization while lowering the barriers to development. We hope that the robotics industry can break free from dependence on a few tech giants and be driven by a global community towards a truly open and sustainable technological ecosystem.