Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.
Updated
January 8, 2026 6:34 PM
A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT
Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.
The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.
The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.
The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.
Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.
This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.
Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.
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Amid AI and tech startups, Eastseabrother proved the power of demand and trust.
Updated
January 23, 2026 10:41 AM
Cats having a jolly good time with a can of tuna. PHOTO: UNSPLASH
At a Silicon Valley pitch event crowded with AI, SaaS and deep-tech startups, the company that stood out was not selling software or algorithms. It was selling pet treats.
Eastseabrother, a premium pet food brand from South Korea, ranked first at a Plug and Play–hosted investor pitch competition in Sunnyvale. The product itself is simple: single-ingredient pet treats made from wild-caught seafood sourced from Korea’s East Sea. The company follows a principle it calls “Only What the Sea Allows”, working directly with regional fishermen while avoiding overfishing. With no additives and minimal processing, what sets Eastseabrother apart is not novelty, but control—over sourcing, supply chains and consistency.
That clarity helped the company walk away with both Best Product and Best Potential. “Investors asked detailed questions about repeat purchase rates and customer feedback, not just our technology or supply chain”, said Eunyul Kim, CEO of Eastseabrother. “That told us the market is shifting—real consumer trust now carries as much weight as a compelling tech narrative”.
What truly caught investors’ attention was not an ambitious vision of the future, but concrete evidence of traction today. Eastseabrother has already secured shelf space in specialty pet stores across California, New York and North Carolina, including an exclusive partnership with EarthWise Pet, a national specialty retail chain. At a consumer showcase at San Francisco’s Ferry Building, the brand recorded the highest on-site sales among all participating companies.
At its core, the pitch was built on simplicity: one ingredient, clear sourcing and a defined customer need. In a market saturated with complex products and abstract claims, that focus and transparency stood out.
The judges’ decision also reflects a broader shift in venture capital thinking. Not every successful startup is built on complex software or high-tech innovation. In categories like pet care—where trust, quality and transparency shape buying behavior—execution and credibility can matter more than technical sophistication.
Today, Eastseabrother has extended its reach beyond the U.S., expanding into Singapore and Hong Kong, with additional plans to grow further in North America as demand for premium pet food rises. And the broader takeaway from this pitch is not that consumer brands are overtaking tech startups. It is that investors are increasingly focused on fundamentals: who is buying, why they are returning and whether the business can sustain itself beyond the pitch deck.