The Hong Kong startup combines 3D-printed reef tiles, biodiversity monitoring and long-term data to help companies manage their impact on marine ecosystems.
Updated
August 10, 2026 5:56 PM
Young corals growing on Archireef's 3D-printed terracotta reef tiles beneath the sea. IMAGE: ARCHIREFF
Anyone who has snorkeled over a coral reef has probably admired its vibrant colours and the marine life swimming between its branches. What is less visible is how easily these ecosystems can be damaged. Rising ocean temperatures and pollution continue to put reefs under pressure, while coastal development creates another threat. Construction can stir up sediment that settles on corals, blocking the sunlight they need and making it harder for damaged reefs to recover.
Protecting marine ecosystems is becoming a shared responsibility. Alongside scientists, governments and conservation groups, businesses whose operations affect the ocean are increasingly expected to understand, manage and reduce their environmental impact. They must also show regulators, banks and investors that they are meeting their environmental commitments.
Archireef helps businesses meet those demands. Founded by Vriko Yu, the Hong Kong-rooted B2B startup specializes in marine ecosystem restoration and ocean nature risk management. Drawing on years of research at the University of Hong Kong, Yu built the company to help organisations understand, measure and reduce their impact on marine ecosystems.
Yu describes Archireef as an “end-to-end ocean nature risk management company”.
“What we offer them is a credible way to meet those obligations and stand behind the results,” she says.
One of the company’s main tools for restoring damaged marine ecosystems is its flagship 3D-printed terracotta reef tile. The tiles provide a stable foundation where young corals can attach, grow and survive over time.
Archireef chose terracotta instead of concrete and other conventional materials because it is pH-neutral and does not release harmful chemicals into the water. Its surface also gives corals a suitable place to attach naturally.
Before placing a single tile in the ocean, the team studies the proposed restoration site to determine whether recovery is possible. It assesses the water quality, biodiversity and general health of the ecosystem. As Yu explains, restoration should begin only where nature has a realistic chance to recover.
“Restoration isn’t about replacing nature or engineering a reef into existence. It is about removing the barriers and giving a degraded system the conditions it needs to begin recovering on its own”, she says.
Once the team selects a site, divers carefully place the tiles on the seabed. Their curves, ridges and crevices imitate the structure of a natural coral reef, creating sheltered spaces for marine species. The design also helps limit sediment build-up, which can block sunlight and smother young corals.
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Archireef adjusts the shape of its reef tiles for each project.
“There’s no universal reef, so there’s no universal tile”, Yu says.
Instead of producing one standard model, the company adapts each design to the coral species, water conditions and surrounding marine environment. A coral restoration project in Hong Kong may therefore require a different tile from one in Saudi Arabia, even when the same manufacturing process is used.
“The biggest adaptation from site to site is usually the match between design, species and environment, not the manufacturing itself ”, Yu explains.
Although the reef tile is Archireef’s most recognizable innovation, Yu sees it as one part of a much broader service.
“The reef tile gets the attention because it’s tangible,” she says. “But the real product is the proof — defensible, audit-ready evidence of nature recovery that an organization can put in a disclosure and stand behind under scrutiny”.
Producing that evidence requires years of biodiversity monitoring. Long after the reef tiles have been installed, Archireef continues to track coral survival and the health of the surrounding marine ecosystem. Its monitoring methods include ecological surveys, AI-assisted species recognition, photogrammetry and environmental DNA testing.
Photogrammetry uses photographs to produce detailed 3D models of the reef. These models allow the team to measure changes in coral growth and reef structure over time.
Environmental DNA, commonly known as eDNA, offers another way to monitor marine biodiversity. Traditional ecological surveys often depend on divers recording the species they can see. eDNA testing instead examines traces of genetic material left in the water by marine organisms, including skin cells, mucus and waste.
By analyzing these traces, Archireef can detect species that may be hidden, difficult to identify or absent during a dive. This gives the company a more complete picture of the ecosystem and how it is changing.
For Archireef, the success of a coral reef restoration project is determined by what happens several years after installation.
“The indicators that matter aren’t the ones that look good on deployment day”, Yu says. “They’re the ones that show, two, three and five years on, whether a genuine ecosystem is establishing and sustaining itself”.
At the company’s first restoration site in Hoi Ha Wan, Hong Kong, around 90% of the transplanted corals remained alive four years after deployment. The long-term survival rate suggests that the ecosystem is recovering rather than simply appearing healthy during the early stages of the project.
Results like these have helped Archireef move beyond academic research and secure collaborations across the public and private sectors. In Hong Kong, the company has worked with government departments and public organisations responsible for coastal infrastructure and marine conservation.
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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.