HyveGeo’s approach to restoring degraded land stands out at the FoodTech Challenge
Updated
January 21, 2026 11:09 AM
Clusters of sandstone buttes in Monument Valley, Colorado Plateau. PHOTO: UNSPLASH
HyveGeo, a climate-focused startup, has been named one of the global winners of the FoodTech Challenge, an international competition designed to surface practical technologies that strengthen food systems in arid and climate-stressed regions.
The FoodTech Challenge (FTC) is based in the UAE and brings together governments, foundations and agri-food institutions to identify early-stage solutions that address food production, land degradation and resource efficiency. Each year, hundreds of startups apply from around the world. In 2026, more than 1,200 teams from 113 countries submitted entries. Only four were selected.
HyveGeo stood out for its approach to one of agriculture’s hardest problems: how to make desert soil usable again. Founded in 2023 by a group of scientists and researchers, the Abu Dhabi-based company focuses on regenerating degraded land using a process built around biochar, a carbon-rich material made from agricultural waste, enhanced with microalgae. The aim is to accelerate soil recovery in environments where water is limited and land has been heavily stressed.
What caught the judges’ attention was not just the technology itself, but the way it links several challenges at once. The system turns waste into a usable soil input, reduces the time it takes for land to become productive and locks carbon into the ground instead of releasing it into the atmosphere. In short, it addresses land degradation, food production and climate pressure through a single framework.
As a winner of the FoodTech Challenge, HyveGeo will share a US$2 million prize with the other selected startups. Beyond funding, the company will also receive support from the UAE’s innovation ecosystem, including research backing, pilot projects, market access and incubation services to help move from testing into wider deployment.
The team’s plans focus on scaling within the UAE first. HyveGeo aims to work across Abu Dhabi’s network of farms and gradually expand into other arid and climate-stressed regions. Its longer-term target is to restore thousands of hectares of degraded land and contribute to carbon removal through soil-based methods.
Placed in a broader context, HyveGeo’s win reflects a shift in how food and climate technologies are being evaluated. Instead of chasing dramatic breakthroughs, competitions like the FTC are increasingly backing systems that connect waste, land, water and carbon into something usable on the ground. Not futuristic agriculture, but practical repair work for environments that can no longer rely on old farming assumptions. If that direction continues, the next wave of food innovation may be less about spectacle and more about quiet, scalable fixes for places where growing food has become hardest.
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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.