Humanoid Bots: The Next Generation Computing Platform from Science Fiction to Reality

Humanoid general-purpose Bots are rapidly transitioning from science fiction to reality. The integration of three major factors - the decline in hardware costs, increased capital investment, and technological breakthroughs in mobility and operational capability - is actively driving significant new platform iterations in the computing field.

Despite the increasing commodification of computing power and hardware, which brings cost advantages to robotic engineering, the industry still faces the limitation of a training data bottleneck. In this context, some projects have begun to leverage decentralized physical artificial intelligence (DePAI) to crowdsource high-precision motion and synthetic data, and build foundational models for Bots. This puts them in a uniquely advantageous position to promote the deployment of humanoid Bots.

From Single Function to Multifunctional Form

The commercialization of robotics technology is not a new concept. Household robots such as vacuum cleaning robots or pet cameras that are well-known to the public are all single-function devices. With the development of artificial intelligence, robots are evolving from single-function machines to multifunctional forms, aiming to adapt to operations in open environments.

Humanoid Bots will gradually upgrade from basic tasks such as cleaning and cooking in the next 5 to 15 years, eventually being capable of more complex jobs such as reception services, firefighting, and even surgical operations.

Recent developments are turning humanoid Bots from science fiction into reality:

• Market Dynamics: Over 100 companies are deploying humanoid Bots
• Hardware technology has successfully crossed the uncanny valley: the new generation of humanoid Bots exhibit fluid and natural movements, allowing them to achieve human-like interactions in real-world environments. Some Bots can walk at speeds of up to 3.3 meters per second, far exceeding the average human walking speed of 1.4 meters per second.
• The new cost paradigm for humanoid Bots: Expected to be lower than the average American labor wage by 2032.

Development Bottlenecks: Training Data from the Real World

Despite the obvious favorable factors in the field of humanoid Bots, the problems of low and insufficient data quality still hinder its large-scale deployment.

Other artificial intelligence entity technologies, such as autonomous driving technology, have basically solved the data problem through cameras and sensors mounted on existing vehicles. Some fleets of autonomous driving systems are capable of generating billions of miles of real road driving data. In the early stages of development, these companies equipped the passenger seat of the vehicles with real human monitors for real-time training while the vehicles were on the road.

However, consumers are unlikely to accept the existence of "Bots nannies." The Bots must have out-of-the-box high performance, which makes data collection prior to deployment crucial. All training must be completed before commercialization, and the scale and quality of the data remain ongoing challenges.

There is a huge gap in the scale of training data across different artificial intelligence fields:

• The training data scale of large language models can exceed 150 trillion text tokens.
• Image generation models utilize billions of labeled video-text pairs.
• In contrast, the largest Bots dataset contains only about 2.4 million interaction records.

This gap explains why robotic technology has not yet achieved a truly foundational model like large language models; the key lies in the incomplete data foundation.

Traditional data collection methods struggle to meet the scalable demands for training data of humanoid robots:

• Simulation: Low cost but lacks real boundary scenarios (the gap between simulation and reality)
• Internet video: Cannot provide the proprioceptive and force feedback environments necessary for Bots learning.
• Real-world data: Although accurate, it requires remote control and manual closed-loop operation, which leads to high costs and a lack of scalability.

Training models in virtual environments is cost-effective and highly scalable, but these models often struggle to deploy in the real world. This problem is referred to as the Sim2Real gap.

For example, a Bot trained in a simulated environment may easily pick up objects that are perfectly lit and have smooth surfaces, but when faced with a chaotic environment, uneven textures, or various unexpected situations that humans are accustomed to in the real world, it often finds itself at a loss.

The Full-Stack Vision of Decentralized Entity AI

Some innovative projects are building vertically integrated software and data platforms aimed at applications for embodied intelligent Bots. The core goal of these projects is to address the data bottleneck issues in the humanoid Bots field, but their vision goes far beyond that. By combining self-developed hardware, multimodal simulation infrastructure, and foundational models, they will become full-stack drivers for achieving embodied intelligence.

These platforms start with proprietary consumer-grade motion capture devices to build a rapidly expanding ecosystem of augmented reality and virtual reality games. Users provide high-fidelity motion data in exchange for online incentive rewards, driving the continuous development of the platform.

What is remarkable is that this growth is entirely due to natural development: users are attracted by the entertainment value of the games, while streamers use motion capture devices to achieve real-time body posture capture of their digital avatars. This spontaneously formed virtuous cycle has realized scalable, low-cost, high-fidelity data production, making the relevant datasets coveted training resources for top Bots companies.

Some projects are still developing a multimodal data platform for a unified fragmented simulation environment. The current simulation field is highly fragmented, with various tools operating independently, each having its advantages but unable to communicate with one another. This fragmented situation delays the R&D process and exacerbates the gap between simulation and reality. By achieving standardization of multiple simulators, these platforms have created a shared virtual infrastructure for the development and evaluation of Bots models. This integration supports consistent benchmarking and significantly enhances the system's scalability and generalization capabilities.

Bots Basic Model

Some projects are developing Bots foundational models, which are being built as core systems of emerging physical artificial intelligence infrastructure. Their positioning is similar to traditional large language foundational models, but aimed at the field of Bots.

By combining crowdsourced motion data with powerful simulation systems and model authorization frameworks, these projects can train foundational models with cross-scenario generalization capabilities. This model supports diverse robotic applications in industrial, consumer, and research fields, enabling generalized deployment under vast and varied data.

The Role of Cryptocurrency Technology in the Physical Artificial Intelligence Tech Stack

Cryptographic technology is building a complete vertical stack for physical world artificial intelligence. Although these projects belong to different levels of the physical AI stack, they share a commonality: most are decentralized physical AI ( DePAI ) projects. DePAI creates an open, composable, permissionless scaling mechanism through token incentives throughout the entire tech stack, and it is this innovation that makes the decentralized development of physical AI a reality.

When the token incentive mechanism officially launches, network participation will accelerate as a key link in the DePAI flywheel effect: users can receive incentives from the project party by purchasing hardware devices, while robot development companies will pay contribution rewards to device holders. This dual incentive will encourage more people to acquire and use related devices. At the same time, the project party will dynamically incentivize the collection of high-value customized behavioral data, thereby bridging the technical gap between simulation and real-world applications (Sim2Real) more effectively.

Conclusion

The revolution of the Bots platform is unstoppable, but like all platforms, its scalable development relies on data support. Some innovative projects are transforming the general public into "miners" of action data. Just as large language models require text labeling support, humanoid Bots need massive action sequence training. Through these efforts, we will break through the final bottleneck and achieve the leap of humanoid Bots from science fiction to reality.