AGIBOT has introduced Worken, a new framework designed to measure how much useful work robots actually complete in real-world environments. The framework aims to provide a different way of evaluating progress in the robotics industry. Instead of focusing on how many robots are manufactured or deployed, Worken looks at their actual productivity, autonomous working time, and the quality of their output.

What Is AGIBOT’s Worken Framework?

The name Worken combines the words Work and Token. The proposed framework focuses on measuring Effective Intelligent Work Hours, giving businesses a way to understand how effectively Physical AI systems perform assigned tasks.

AGIBOT believes that counting deployed robots alone does not provide a complete picture of their usefulness. For example, two factories may operate the same number of robots but achieve very different production results. Robots that work independently for longer periods and consistently complete tasks with minimal human assistance deliver greater productivity.

The same idea can also apply to logistics and service robots. Simply moving around a warehouse or interacting with customers is not enough. These machines must successfully complete their assigned tasks.

How Does Worken Calculate Robot Productivity?

AGIBOT’s proposed formula combines three factors: the number of active robots, their autonomous working hours and their output efficiency ratio.

The first factor counts robots that are actively performing useful tasks rather than machines that have only been manufactured, delivered or installed. The second measures how long robots work independently, excluding waiting periods and continuous human control. The third evaluates completed work against an agreed productivity benchmark.

For example, a manufacturing facility with 10 active robots, six autonomous working hours per robot and an output efficiency ratio of 0.8 would generate 48 Worken.

This approach aims to distinguish between machines that remain powered on and those that consistently deliver acceptable results.

Potential Applications in Manufacturing and Other Industries

Manufacturing could be one of the first areas where Worken becomes useful because production tasks often have clearly defined targets. Businesses could use the framework to monitor robotic productivity, compare similar operations and identify where machines lose productive working time.

It could also help companies evaluate software updates by checking whether robots complete more tasks independently or require less human intervention.

AGIBOT’s existing industrial applications, including material-handling operations at Longcheer, provide potential real-world environments for exploring the framework.

The concept could eventually extend to service robots, where productivity might be measured through completed guidance tasks, customer assistance and successful visitor interactions.

Worken Is Still an Evolving Framework

AGIBOT describes Worken as a developing measurement framework rather than an established universal standard. Its practical usefulness will depend on reliable operational data, clearly defined tasks and suitable performance benchmarks.

It is also not intended to replace other important measures such as safety, reliability, operating costs and output quality. Furthermore, Worken does not represent a universal monetary value for robotic labour.

If developed successfully, the framework could help businesses make better decisions about robotic investments and understand how software and AI improvements increase productivity over time.

Ultimately, AGIBOT’s approach highlights a broader shift in robotics: measuring progress not just by how many robots enter the workplace, but by how much useful work they actually deliver.

Read More:

• AGIBOT Wins 46 Medals at World Humanoid Robot Games, Takes 18 Gold
• Humanoid Robots Are Scaling Fast, And AGIBOT from China Is Leading the Charge
• Chinese Robot Company AGIBOT Took 39% of the Global Humanoid Market in 2025

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AgiBot, a robotics company focused on embodied intelligence, has achieved a major milestone by successfully deploying its Real-World Reinforcement Learning (RW-RL) system on a pilot production line with Longcheer Technology. This marks the first-ever real-world application of reinforcement learning in industrial robotics, bridging years of AI research with practical use in manufacturing.

Transforming the Future of Manufacturing

For decades, precision manufacturing has relied on rigid automation systems that require complex fixtures, extensive tuning, and high setup costs. These systems are difficult to reconfigure when new products are introduced or production layouts change. Even modern robots equipped with vision and force-control systems often suffer from high sensitivity to parameter changes, resulting in long deployment times and expensive maintenance.

AgiBot’s RW-RL solution aims to solve these long-standing challenges by enabling robots to learn directly from real-world interactions. Instead of depending on pre-programmed instructions, the robots can adapt to changing conditions on the factory floor and refine their performance in real time.

How AgiBot’s RW-RL System Works

The Real-World Reinforcement Learning system allows robots to acquire new skills within minutes instead of weeks. Once deployed, they can maintain industrial-grade stability and perform tasks continuously without performance degradation. The system requires minimal hardware adjustments when switching between tasks or products, significantly reducing downtime and costs.

AgiBot’s technology also enables robots to autonomously adjust for part variations and manufacturing tolerances, ensuring consistent precision. This adaptability makes the system ideal for flexible, multi-product production lines, paving the way for smarter and more versatile factories.

From Research to Real-World Deployment

The RW-RL breakthrough is built on years of academic research into the stability and efficiency of reinforcement learning. Led by Dr. Jianlan Luo, Chief Scientist at AgiBot, the team successfully transformed complex AI algorithms into a deployable industrial system. The pilot deployment with Longcheer Technology validated the system’s performance under near-production conditions, confirming it as the first verified industrial application of reinforcement learning in manufacturing robotics.

Following the successful pilot, AgiBot and Longcheer plan to expand the use of RW-RL to consumer electronics and automotive component production. Their goal is to create modular, easily deployable robotic solutions that can integrate seamlessly into existing factory systems.

With this achievement, AgiBot has not only demonstrated a major advancement in robotic intelligence but also taken a crucial step toward the future of self-learning, adaptable manufacturing systems, bringing the promise of embodied AI closer to industrial reality.

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