Indoor farms, also known as vertical farms, are popular among agricultural researchers and are expanding across the agricultural industry. Some benefits they have over outdoor farms include:
Additionally, some studies indicate that indoor farms produce more nutritious food for urban communities.
However, these farms are often inaccessible to birds, bees, and other natural pollinators, leaving the pollination process to humans. The tedious process must be completed by hand for each flower to ensure the indoor crop flourishes.
Ai-Ping Hu, a principal research engineer at the Georgia Tech Research Institute (GTRI), has spent years exploring methods to efficiently pollinate flowering plants and food crops in indoor farms to find a way to efficiently pollinate flower plants and food crops in indoor farms.
Hu, Assistant Professor Shreyas Kousik of the George W. Woodruff School of Mechanical Engineering, and a rotating group of student interns have developed a robot prototype that may be up to the task.
The robot can efficiently pollinate plants that have both male and female reproductive parts. These plants only require pollen to be transferred from one part to the other rather than externally from another flower.
Natural pollinators perform this task outdoors, but Hu said indoor farmers often use a paintbrush or electric tootbrush to ensure these flowers are pollinated.
An early challenge the research team addressed was teaching the robot to identify the “pose” of each flower. Pose refers to a flower’s orientation, shape, and symmetry. Knowing these details ensures precise delivery of the pollen to maximize reproductive success.
“It’s crucial to know exactly which way the flowers are facing,” Hu said.
“You want to approach the flower from the front because that’s where all the biological structures are. Knowing the pose tells you where the stem is. Our device grasps the stem and shakes it to dislodge the pollen.
“Every flower is going to have its own pose, and you need to know what that is within at least 10 degrees.”
Harsh Muriki is a robotics master’s student at Georgia Tech’s School of Interactive Computing, who used computer vision to solve the pose problem while interning for Hu and GTRI.
Muriki attached a camera to a FarmBot to capture images of strawberry plants from dozens of angles in a small garden in front of Georgia Tech’s Food Processing Technology Building. The FarmBot is an XYZ-axis robot that waters and sprays pesticides on outdoor gardens, though it is not capable of pollination.
“We reconstruct the images of the flower into a 3D model and use a technique that converts the 3D model into multiple 2D images with depth information,” Muriki said. “This enables us to send them to object detectors.”
Muriki said he used a real-time object detection system called YOLO (You Only Look Once) to classify objects. YOLO is known for identifying and classifying objects in a single pass.
Ved Sengupta, a computer engineering major who interned with Muriki, fine-tuned the algorithms that converted 3D images into 2D.
“This was a crucial part of making robot pollination possible,” Sengupta said. “There is a big gap between 3D and 2D image processing.
“There’s not a lot of data on the internet for 3D object detection, but there’s a ton for 2D. We were able to get great results from the converted images, and I think any sector of technology can take advantage of that.”
Sengupta, Muriki, and Hu co-authored a paper about their work that was accepted to the 2025 International Conference on Robotics and Automation (ICRA) in Atlanta.
The pollination robot, built in Kousik’s Safe Robotics Lab, is now in the prototype phase.
Hu said the robot can do more than pollinate. It can also analyze each flower to determine how well it was pollinated and whether the chances for reproduction are high.
“It has an additional capability of microscopic inspection,” Hu said. “It’s the first device we know of that provides visual feedback on how well a flower was pollinated.”
For more information about the robot, visit the Safe Robotics Lab project page.
]]>A Georgia Tech research led by Ai-Ping Hu and Shreyas Kousik team is working to solve that. A robot they've developed can efficiently pollinate plants that have both male and female reproductive parts. These plants only require pollen to be transferred from one part to the other rather than externally from another flower.
]]>In these contests, a handler directs a trained dog with whistle signals to guide a small group of sheep across a field and sometimes split the flock cleanly into two groups. But sheep do not always cooperate.
Researchers at the Georgia Institute of Technology studied how handler–dog teams manage these unpredictable flocks in sheepdog trials and found principles that extend beyond livestock herding.
In a study published in Science Advances as the cover feature, the researchers applied those insights to computer simulations showing how similar strategies could improve the control of robot swarms, autonomous vehicles, AI agents, and other networked systems where many machines must coordinate their actions despite uncertain conditions.
Group Movement Dynamics
“Birds, bugs, fish, sheep, and many other organisms move in groups because it benefits individuals, including protection from predators,” said Saad Bhamla, an associate professor in Georgia Tech’s School of Chemical and Biomolecular Engineering. “The puzzle is that the ‘group’ is not a single organism. It is built from many individuals, each making local, imperfect decisions.”
When a predator threatens a herd of sheep, individuals near the edge often move toward the center to reduce their own risk, Bhamla explained. “This is ‘selfish herd’ behavior,” he said. “Shepherds exploit that instinct using trained dogs.”
From examining hours of contest footage, the researchers found that controlling small groups of sheep can be harder than managing large ones. A larger group, with more sheep protected in the center, may behave more coherently than a small group as the animals constantly shift between two instincts: “follow the group” and “flee the dog.”
“That switching behavior makes the group unpredictable,” said Tuhin Chakrabortty, a former postdoctoral researcher in the Bhamla Lab who co-led the study.
Looking closely at how dogs and their handlers guide small groups, the researchers found that unpredictability in the flock’s behavior does not always make control harder. “Under the right conditions, that ‘noisy’ behavior might actually be a benefit,” Bhamla said.
Successful Sheep Herding
Sheepdog handlers categorize sheep by how strongly they respond to a dog’s threatening pressure. Some very responsive sheep might panic under too much pressure, while others might ignore mild pressure and require stronger positioning by the dog.
The researchers observed that successful control often followed a two-step pattern. First, the dog subtly influenced the sheep’s orientation while the animals were mostly standing still. Once the flock was aligned in the desired direction, the dog increased pressure to trigger movement. The timing of those actions was critical, because alignment within a small group could disappear quickly as individuals switched between instincts.
“In our simulations, increasing pressure makes the flock reach the desired orientation faster, but how long the flock stays aligned is set mainly by noise,” Chakrabortty said. “In essence, dogs can steer the direction, but they can’t hold that decision indefinitely, so timing matters.”
Developing Computer Models
To understand the broader implications of that behavior, the team developed computer models that captured how sheep respond both to the dog and to one another. The models allowed the researchers to test different strategies for guiding groups whose members make independent decisions under uncertainty.
They then applied those ideas to simulations of robotic swarms. Engineers often design such systems so that each robot blends signals from all nearby robots before deciding how to move. While that approach works well when signals are clear, it can break down when information is noisy or conflicting, Bhamla explained.
To explain why that switching strategy can work under noisy conditions, the researchers used an analogy of a smoke-filled room where only one person can see the exit, and no one knows who that person is. If everyone polls everyone else and averages the guesses, the one correct signal can get diluted by many noisy ones.
“That’s the counterintuitive part. When only one person has the right information, averaging can wash out the signal. But if you follow one person at a time, and keep switching who that is, the right information can spread through the crowd,” Bhamla said.
Building on that idea, the researchers tested a strategy inspired by the switching behavior they observed in sheep. In the simulations, each robot paid attention to just one source at a time (either a guiding signal or a neighboring robot) and switched that source from one step to the next.
Under noisy conditions, this switching strategy required less effort to keep the group moving along a desired path than either averaging-based strategies or fixed leader-follower strategies.
The researchers call their approach the Indecisive Swarm Algorithm. The name reflects a counterintuitive insight: allowing influence to shift among individuals over time can make groups easier to guide when conditions are uncertain.
“Our findings suggest that the same dynamics that make small animal groups unpredictable may also offer new ways to control complex engineered systems,” Bhamla said.
CITATION: Tuhin Chakrabortty and Saad Bhamla, “Controlling noisy herds: Temporal network restructuring improves control of indecisive collectives,” Science Advances, 2026
This research was funded in part by Schmidt Sciences as part of a Schmidt Polymath grant to Saad Bhamla.
Even when engineers have a working device and brain, called a controller, changes and improvements to the exoskeleton system typically mean data collection and training start all over again. The process is expensive and makes bringing fully functional exoskeletons or robotic limbs into the real world largely impractical.
Not anymore, thanks to Georgia Tech engineers and computer scientists.
They’ve created an artificial intelligence tool that can turn huge amounts of existing data on how people move into functional exoskeleton controllers. No data collection, retraining, and hours upon hours of additional lab time required for each specific device.
Their approach has produced an exoskeleton brain capable of offering meaningful assistance across a huge range of hip and knee movements that works as well as the best controllers currently available. Their worked was published Nov. 19 in Science Robotics.
Full details on the College of Engineering website.
]]>World foundation models (WFMs) enable physical AI systems to learn and operate within synthetic worlds created by generative artificial intelligence (genAI). For example, these models use predictive capabilities to generate up to 30 seconds of video that accurately reflects the real world.
The new framework, developed by a Georgia Tech researcher, enhances the processing speed of the neural networks that simulate these real-world environments from text, images, or video inputs.
The neural networks that make up the architectures of large language models like ChatGPT and visual models like Sora process contextual information using the “attention mechanism.”
Attention refers to a model’s ability to focus on the most relevant parts of input.
The Neighborhood Attention Extension (NATTEN) allows models that require GPUs or high-performance computing systems to process information and generate outputs more efficiently.
Processing speeds can increase by up to 2.6 times, said Ali Hassani, a Ph.D. student in the School of Interactive Computing and the creator of NATTEN. Hassani is advised by Associate Professor Humphrey Shi.
Hassani is also a research scientist at Nvidia, where he introduced NATTEN to Cosmos — a family of WFMs the company uses to train robots, autonomous vehicles, and other physical AI applications.
“You can map just about anything from a prompt or an image or any combination of frames from an existing video to predict future videos,” Hassani said. “Instead of generating words with an LLM, you’re generating a world.
“Unlike LLMs that generate a single token at a time, these models are compute-heavy. They generate many images — often hundreds of frames at a time — so the models put a lot of work on the GPU. NATTEN lets us decrease some of that work and proportionately accelerate the model.”
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