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

On February 27, a podcast discussion on "Building Decentralized Physical Artificial Intelligence" caught the industry's attention. This discussion delved into the challenges and opportunities that decentralized physical infrastructure networks (DePIN) face 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 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 analyze the key points from the discussion, explore the issues faced by DePIN robotic technology, analyze the main obstacles to scaling decentralized robotics, and discuss the advantages of DePIN over centralized methods. Finally, we will also explore the future development prospects of DePIN robotic technology.

The main bottleneck of DePIN smart robots

Bottleneck One: Data

Unlike the "online" AI large models that rely heavily on vast amounts of internet data for training, embodied AI requires interaction with the real world to develop intelligence. Currently, there is no large-scale infrastructure established for this, and there is a lack of consensus 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 with high costs and labor intensity.
2. Synthetic Data (Simulated Data): Suitable for training robots to move in complex terrains, but not effective for tasks that are highly variable.
3. Video Learning: Allow AI models to learn by observing videos of the real world, but lacking direct physical interaction feedback.

Bottleneck Two: Level of Autonomy

To make robotics truly practical, the success rate needs to be close to 99.99% or even higher. However, improving the accuracy by even 0.001% requires exponential amounts of time and effort. The advancement of robotics is not linear, but exponential in nature, with each step forward significantly increasing the difficulty.

Bottleneck Three: Hardware Limitations

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

• Lack of highly sensitive tactile sensors
• Difficulties in object occlusion recognition
• The actuator design is not biologically mimetic enough, resulting in stiff movements and potential dangers.

Bottleneck Four: Difficulty in Hardware Expansion

The realization of intelligent robot technology requires the deployment of physical devices in the real world, which poses significant capital challenges. Currently, only financially strong large companies can afford large-scale experiments, and the cost of the most efficient humanoid robots still reaches tens of thousands of dollars, making large-scale popularization difficult.

Bottleneck Five: Assessing Effectiveness

Evaluating physical AI requires long-term, large-scale deployment in the real world, a process that is time-consuming and complex. Unlike online AI large models that can be tested quickly, the true performance of robotic intelligence technology can only be validated through long-term practical application.

Bottleneck Six: Human Resource Demand

In the development of robot AI, human labor remains indispensable. Human operators are needed to provide training data, maintenance teams keep the robots running, and researchers continuously optimize the AI models. This ongoing human intervention is a major challenge that DePIN must address.

Future Outlook: The Breakthrough Moment of Robotics Technology

Although the adoption of general-purpose robot AI is still some way off, the advancements in DePIN robotics technology offer hope. The scale and coordination of decentralized networks can alleviate capital burdens and accelerate the data collection and evaluation process.

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

In addition, the new AI agents demonstrate an innovative profit model for 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.

Summary

The development of AI in robotics involves multiple aspects, including algorithms, hardware upgrades, data accumulation, funding support, and human participation. The establishment of the DePIN robot network means that, with the power of decentralized networks, robot data collection, computational resources, and capital investment can be coordinated globally. This not only accelerates AI training and hardware optimization but also lowers the development threshold, 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 rather be driven by a global community, moving towards a truly open and sustainable technological ecosystem. The development of DePIN may bring revolutionary breakthroughs to robotics technology, pushing the industry towards a more democratized and innovative future.