TCL set the stage for a major technological leap at the 2025 Global Technology Innovation Conference (TIC) held in Guangzhou, China. With the theme “AI for Real,” the 12th annual gathering focused on turning artificial intelligence into practical solutions that deliver measurable benefits. Researchers, industry experts, and global partners explored how AI can reshape manufacturing, consumer electronics, and green innovation. TCL highlighted its goal of creating AI that brings economic value, enhances daily life, and supports a sustainable future.

Practical AI Adoption

TCL Founder and Chairman Li Dongsheng stressed that AI must produce real, tangible value across industries. He shared that TCL’s AI solutions are expected to generate more than US$140 million in economic benefits in 2025. He also urged deeper cross-sector collaboration to accelerate AI-powered industrial transformation.
COO Kevin Wang reinforced this message, emphasizing the shift from conceptual AI to real-world deployment. He noted that TIC 2025 aims to define the next chapter of industrial AI development by focusing on measurable and sustainable outcomes.

Major Breakthroughs in Intelligent Manufacturing

A highlight of the event was TCL CSOT’s launch of X-Intelligence 3.0, the world’s first large AI model built specifically for display technologies. It improves issue analysis by 20% and boosts materials development efficiency by 30%. TCL also introduced its APEX philosophy, which focuses on eye protection, eco-friendly materials, and imaginative display experiences.

TCL confirmed a major milestone with its 8.6-generation inkjet-printed OLED (IJP OLED) production line, a US$4 billion project now ready for mass manufacturing. New innovations included the world’s first 5.65-inch Real Stripe RGB OLED for smartphones and an advanced 28-inch foldable portable display.

Dr. Yan Xiaolin, CTO of TCL Technology and TCL CSOT, highlighted the company’s major progress in industrial automation. He explained that TCL has now achieved complete end-to-end automation, covering Auto Defect Classification (ADC), evaluation, and Auto Defect Repair (ADR). He noted that this full-scale deployment has already generated more than US$7 million in annual revenue, demonstrating the real commercial value of AI-driven manufacturing.

AI-Powered Smart Living Products

In consumer technology, TCL expanded its AI smart living ecosystem. The FreshIN AI air conditioners reduce energy use by up to 40% and improve sleep quality. AI refrigerators maintain precise temperatures, while AI Super Drum washing machines use sensors and the Fuxi LLM to customize wash cycles. TCL’s AI TVs can understand complex voice commands and refine visuals frame by frame.

The company also showcased RayNeo X3 Pro AR glasses, featuring real-time subtitles, translation, and visualized AI assistance. Another standout innovation was TCL AiMe, the world’s first modular AI home robot designed for emotional interaction and family companionship.

R&D Power and Global Collaboration

TCL highlighted its strong innovation capacity, backed by US$8.4 billion in R&D investment over six years, more than 20,000 researchers, and over 110,000 patents. Its global partnerships continue to expand through major initiatives such as the US$2.8 billion Rising Sun Project, strengthening ecosystem-wide technological development.

Commitment to Green and Sustainable Technology

Sustainability was another major theme. TCL emphasized the role of AI in promoting energy-efficient products, responsible material sourcing, and cleaner manufacturing. The TCLGreen initiative aims to merge technology, clean energy, and eco-friendly design to support both planetary health and consumer wellbeing.

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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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