The Integration of DePIN and Embodied Intelligence: Challenges and Prospects

With the rapid development of artificial intelligence technology, the application of Decentralized Physical Infrastructure Networks (DePIN) in the field of robotics has attracted widespread attention. Recently, a discussion on "Building Decentralized Physical Artificial Intelligence" delved into the challenges and opportunities that DePIN faces in the field of robotics technology. 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 heavily on vast amounts of internet data, DePIN robotics 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 robotics technology, explore why DePIN has advantages over centralized methods, and look ahead to the future development trends of DePIN robotics technology.

The Main Bottlenecks of DePIN Smart Robots

1. Data Collection and Quality

Unlike traditional AI large models that rely heavily on vast amounts of internet data, embodied AI requires direct interaction with the real world in order to develop intelligence. However, there is currently a lack of large-scale infrastructure to collect such data, and there is no consensus in the industry on how to effectively gather this data. The data collection for embodied AI is mainly divided into three categories:

• Human-operated data: high quality, capable of capturing video streams and action labels, but costly and labor-intensive.
• Synthetic data (simulated data): Suitable for training in specific fields, such as complex terrain movement, but difficult to simulate ever-changing task scenarios.
• Video Learning: Learning through observing real-world videos, but lacking direct physical interaction feedback.

2. Autonomy Level

To achieve commercial applications of robotics technology, the success rate needs to be close to 99.99% or even higher. However, every increase of 0.001% in accuracy requires exponentially more time and effort. The advancement of robotics technology is not linear but exponential in nature; with each step forward, the difficulty increases significantly.

3. Hardware Limitations

Even with advanced AI models, existing robotic hardware has not fully supported true autonomy. The main issues include:

• Limitations of tactile sensors: Current technology is far from the sensitivity of human fingertips.
• Difficulty in identifying occlusions: Robots find it challenging to handle partially occluded objects.
• Actuator design flaws: Most humanoid robots have their actuators placed directly on the joints, resulting in stiff movements and potential dangers.

4. Hardware expansion difficulty

The implementation 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. Even the most efficient humanoid robots can cost tens of thousands of dollars, making widespread adoption difficult.

5. Assessing Effectiveness

Assessing physical AI requires long-term and large-scale deployment in the real world, a process that is time-consuming and complex. Unlike online AI large models that can be evaluated quickly, the performance evaluation of robotic AI requires a significant amount of time and resources.

6. Human Resource Demand

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

Future Outlook: Breakthrough Moment in Robotics Technology

Although the widespread adoption of general-purpose robotic AI is still some way off, the progress of DePIN robotic technology offers hope. The scale and coordination of decentralized networks can alleviate capital burdens and accelerate data collection and evaluation processes.

1. Data collection and evaluation acceleration: Decentralized networks can run in parallel and collect data, significantly improving efficiency.

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

3. New Profit Model: The decentralized robot technology network showcases new profit possibilities, such as self-operating AI agents maintaining their own finances through token incentives.

4. Open Collaboration: The establishment of the DePIN robotic network means that robot data collection, computing resources, and capital investment can be coordinated globally, lowering the development threshold and allowing more participants to join.

In summary, the development of robot AI not only relies on algorithms but also involves hardware upgrades, data accumulation, financial support, and human participation. The establishment of the DePIN robot network is expected to break the limitations of the traditional robotics industry and create a more open and sustainable technological ecosystem. With the joint efforts of the global community, we look forward to witnessing a truly breakthrough moment in robotic technology.