AgiBot says its RW-RL system enables robots to quickly learn complex assembly tasks. | Credit: Agibot
AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology.
The pilot project marks AgiBot’s first application of real-world reinforcement learning (RW-RL) on an active line, connecting advanced AI innovation with large-scale production and signaling a new phase in the evolution of intelligent automation for precision manufacturing.
Tackling the core challenges of flexible manufacturing
For decades, precision manufacturing lines have relied on rigid automation systems that demand complex fixture design, extensive tuning, and costly reconfiguration. Even advanced “vision + force-control” solutions have struggled with parameter sensitivity, long deployment cycles, and maintenance complexity.
AgiBot said its RW-RL system is addressing these long-standing pain points by enabling robots to learn and adapt directly on the factory floor. Within just tens of minutes, robots can acquire new skills, achieve stable deployment, and maintain long-term performance without degradation, it said.
During line changes or model transitions, only minimal hardware adjustments and standardized deployment steps are required. This can dramatically improve flexibility while cutting time and cost, said the company, which launched its Agibot G2 robot last month.
Agibot G2 provides embodied intelligence and demonstrates guided tours in a museum. Source: AgiBot
AgiBot lists advantages of Real-World Reinforcement Learning
- Rapid deployment: Training time for new skills is reduced from weeks to minutes, achieving exponential gains in efficiency, asserted AgiBot.
- High adaptability: The system autonomously compensates for common variations such as part position and tolerance shifts, maintaining industrial-grade stability and a 100% task completion rate over extended operation.
- Flexible reconfiguration: Task or product changes can be accommodated through fast retraining, without custom fixtures or tooling, overcoming the long-standing “rigid automation versus variable demand” dilemma in consumer electronics manufacturing.
AgiBot claimed that its system exhibits generality across workspace layouts and production lines, enabling quick transfer and reuse across diverse industrial scenarios. This milestone signifies a deep integration between perception-decision intelligence and motion control, representing a critical step toward unifying algorithmic intelligence and physical execution, said the company.
Likewise, the solution exhibits strong generality across workspace layouts and production lines, allowing quick transfer and reuse across diverse industrial scenarios. This milestone signifies a deep integration between perception-decision intelligence and motion control, representing a crucial step toward unifying algorithmic intelligence and physical execution, said AgiBot.
Unlike many laboratory demonstrations, the company said its system was validated under near-production conditions, completing the loop from cutting-edge research to industrial-grade verification.
From research breakthrough to industrial reality
In recent years, the robotics and AI research community has made significant progress in advancing reinforcement learning toward greater stability, efficiency, and real-world applicability. Building on these advances, Dr. Jianlan Luo, chief scientist at Agibot, and his team have published research demonstrating that reinforcement learning can achieve reliable and high-performance results directly on physical robots.
At AgiBot, this foundation evolved into a deployable RW-RL system, integrating advanced algorithms with control and hardware stacks. The company said its system achieves stable, repeatable learning on real machines—marking an important step in bridging academic research and industrial deployment.
AgiBot expands real-world applications
The validation has now been successfully demonstrated on a pilot production line in collaboration with Longcheer Technology.
Moving forward, AgiBot and Longcheer plan to extend real-world reinforcement learning to a broader range of precision manufacturing scenarios, including consumer electronics and automotive components. The focus will be on developing modular, rapidly deployable robot solutions that integrate seamlessly with existing production systems.
AgiBot, also known as Zhiyuan Robotics, recently launched the LinkCraft application to reduce the skills required to program robots. LinkCraft is a platform for robot motion creation, allowing the user to use video as a training asset.
At the recent iROS 2025 event, the first “AgiBot World Challenge @ IROS 2025” drew 431 teams from 23 countries worldwide, with winning teams from Tsinghua University, South China University of Technology, and the University of Hong Kong.
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