Robotics

Georgia Tech Team Takes Second Place at ICRA Robot Teleoperation Contest

dgivens8 — Tue, 29 Jul 2025 12:58:25 +0000

Georgia Tech Team Takes Second Place at ICRA Robot Teleoperation Contest dgivens8 Tue, 07/29/2025 - 08:58

An algorithmic breakthrough from School of Interactive Computing researchers that earned a Meta partnershipdrew more attention at the IEEE International Conference on Robotics and Automation (ICRA).

Meta announced in February its partnership with the labs of professors Danfei Xu and Judy Hoffman on a novel computer vision-based algorithm called EgoMimic. It enables robots to learn new skills by imitating human tasks from first-person video footage captured by Meta’s Aria smart glasses.

Xu’s Robot Learning and Reasoning Lab (RL2) displayed EgoMimic in action at ICRA May 19-23 at the World Congress Center in Atlanta.

Lawrence Zhu, Pranav Kuppili, and Patcharapong “Elmo” Aphiwetsa — students from Xu’s lab — used Egomimic to compete in a robot teleoperation contest at ICRA. The team finished second in the event titled What Bimanual Teleoperation and Learning from Demonstration Can Do Today, earning a $10,000 cash prize.

Teams were challenged to perform tasks by remotely controlling a robot gripper. The robot had to fold a tablecloth, open a vacuum-sealed container, place an object into the container, and then reseal it in succession without any errors.

Teams completed the tasks as many times as possible in 30 minutes, earning points for each successful attempt.

The competition also offered different challenge levels that increased the points awarded. Teams could directly operate the robot with a full workstation view and receive one point for each task completion. Or, as the RL2 team chose, teams could opt for the second challenge level.

The second level required an operator to control the task with no view of the workstation except for what was provided to through a video feed. The RL2 team completed the task seven times and received double points for the challenge level.

The third challenge level required teams to operate remotely from another location. At this level, teams could earn four times the number of points for each successful task completed. The fourth level challenged teams to deploy an algorithm for task performance and awarded eight points for each completion.

Using two of Meta’s Quest wireless controllers, Zhu controlled the robot under the direction of Aphiwetsa, while Kuppili monitored the coding from his laptop.

“It’s physically difficult to teleoperate for half an hour,” Zhu said. “My hands were shaking from holding the controllers in the air for that long.”

Being in constant communication with Aphiwetsa helped him stay focused throughout the contest.

“I helped him strategize the teleoperation and noticed he could skip some of the steps in the folding,” Aphiwetsa said. “There were many ways to do it, so I just told him what he could fix and how to do it faster.”

Zhu said he and his team had intended to tackle the fourth challenge level with the EgoMimic algorithm. However, due to unexpected time constraints, they decided to switch to the second level the day before the competition due to unexpected time constraints.

“I think we realized the day before the competition training the robot on our model would take a huge amount of time,” Zhu said. “We decided to go for the teleoperation and started practicing.”

He said the team wants to tackle the highest challenge level and use a training model for next year’s ICRA competition in Vienna, Austria.

ICRA is the world’s largest robotics conference, and Atlanta hosted the event for the third time in its history, drawing a record-breaking attendance of over 7,000.

Subtitle

Summary sentence

A Georgia Tech team earned second place in the ICRA Robot Teleoperation Contest for their EgoMimic algorithm, which allows robots to learn skills by mimicking human tasks from first-person video.

Summary

Students from Georgia Tech's Robot Learning and Reasoning Lab earned second place and a $10,000 cash prize in a robot teleoperation contest at the 2025 International Conference on Robotics and Automation in Atlanta. The RL2 lab announced a partnership with Meta in February on a novel computer vision-based algorithm called EgoMimic. It enables robots to learn new skills by imitating human tasks from first-person video footage captured by Meta’s Aria smart glasses.

Dateline

Wed, 06/11/2025 - 12:00

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Thu, 06/12/2025 - 07:52

Rule the Pool This Summer and Make the Biggest Splash

admin — Fri, 16 May 2025 16:37:41 +0000

Rule the Pool This Summer and Make the Biggest Splash admin Fri, 05/16/2025 - 12:37

Want to create the biggest splash in the pool this summer? Forget the bellyflop and the cannonball.

“Popping the Manu” will make you a winner.

Georgia Tech researchers studied dives by the Māori, the indigenous people of New Zealand, who have made Manu jumping a cultural tradition. By hitting the water in a “V” shape, then quickly extending their bodies underwater, they’ve perfected the art of huge splashes.

See a video on how to make the splash and read the entire story on the College of Engineering homepage.

Subtitle

Georgia Tech roboticists explain the physics of epic pool jumps and the New Zealanders who have mastered them

Summary sentence

By hitting the water in a “V” shape, then quickly extending their bodies underwater, the Māori have perfected the art of huge splashes.

Summary

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Fri, 05/16/2025 - 12:00

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Fri, 05/16/2025 - 12:37

Thesis on Human-Centered AI Earns Honors from International Computing Organization

dgivens8 — Tue, 22 Apr 2025 14:30:41 +0000

Thesis on Human-Centered AI Earns Honors from International Computing Organization dgivens8 Tue, 04/22/2025 - 10:30

A Georgia Tech alum’s dissertation introduced ways to make artificial intelligence (AI) more accessible, interpretable, and accountable. Although it’s been a year since his doctoral defense, Zijie (Jay) Wang’s (Ph.D. ML-CSE 2024) work continues to resonate with researchers.

Wang is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI). The award recognizes Wang for his lifelong work on democratizing human-centered AI.

“Throughout my Ph.D. and industry internships, I observed a gap in existing research: there is a strong need for practical tools for applying human-centered approaches when designing AI systems,” said Wang, now a safety researcher at OpenAI.

“My work not only helps people understand AI and guide its behavior but also provides user-friendly tools that fit into existing workflows.”

Wang’s dissertation presented techniques in visual explanation and interactive guidance to align AI models with user knowledge and values. The work culminated from years of research, fellowship support, and internships.

Wang’s most influential projects formed the core of his dissertation. These included:

CNN Explainer: an open-source tool developed for deep-learning beginners. Since its release in July 2020, more than 436,000 global visitors have used the tool.
DiffusionDB: a first-of-its-kind large-scale dataset that lays a foundation to help people better understand generative AI. This work could lead to new research in detecting deepfakes and designing human-AI interaction tools to help people more easily use these models.
GAM Changer: an interface that empowers users in healthcare, finance, or other domains to edit ML models to include knowledge and values specific to their domain, which improves reliability.
GAM Coach: an interactive ML tool that could help people who have been rejected for a loan by automatically letting an applicant know what is needed for them to receive loan approval.
Farsight: a tool that alerts developers when they write prompts in large language models that could be harmful and misused.

“I feel extremely honored and lucky to receive this award, and I am deeply grateful to many who have supported me along the way, including Polo, mentors, collaborators, and friends,” said Wang, who was advised by School of Computational Science and Engineering (CSE) Professor Polo Chau.

“This recognition also inspired me to continue striving to design and develop easy-to-use tools that help everyone to easily interact with AI systems.”

Like Wang, Chau advised Georgia Tech alumnus Fred Hohman (Ph.D. CSE 2020). Hohman won the ACM SIGCHI Outstanding Dissertation Award in 2022.

Chau’s group synthesizes machine learning (ML) and visualization techniques into scalable, interactive, and trustworthy tools. These tools increase understanding and interaction with large-scale data and ML models.

Chau is the associate director of corporate relations for the Machine Learning Center at Georgia Tech. Wang called the School of CSE his home unit while a student in the ML program under Chau.

Wang is one of five recipients of this year’s award to be presented at the 2025 Conference on Human Factors in Computing Systems (CHI 2025). The conference occurs April 25-May 1 in Yokohama, Japan.

SIGCHI is the world’s largest association of human-computer interaction professionals and practitioners. The group sponsors or co-sponsors 26 conferences, including CHI.

Wang’s outstanding dissertation award is the latest recognition of a career decorated with achievement.

Months after graduating from Georgia Tech, Forbes named Wang to its 30 Under 30 in Science for 2025 for his dissertation. Wang was one of 15 Yellow Jackets included in nine different 30 Under 30 lists and the only Georgia Tech-affiliated individual on the 30 Under 30 in Science list.

While a Georgia Tech student, Wang earned recognition from big names in business and technology. He received the Apple Scholars in AI/ML Ph.D. Fellowship in 2023 and was in the 2022 cohort of the J.P. Morgan AI Ph.D. Fellowships Program.

Along with the CHI award, Wang’s dissertation earned him awards this year at banquets across campus. The Georgia Tech chapter of Sigma Xi presented Wang with the Best Ph.D. Thesis Award. He also received the College of Computing’s Outstanding Dissertation Award.

“Georgia Tech attracts many great minds, and I’m glad that some, like Jay, chose to join our group,” Chau said. “It has been a joy to work alongside them and witness the many wonderful things they have accomplished, and with many more to come in their careers.”

Summary sentence

Zijie (Jay) Wang (Ph.D. ML-CSE 2024) is a recipient of the 2025 Outstanding Dissertation Award from the Association for Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI).

Summary

Dateline

Thu, 04/17/2025 - 12:00

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Tue, 04/22/2025 - 10:29

Securing Tomorrow’s Autonomous Robots Today

admin — Wed, 26 Mar 2025 01:18:41 +0000

Securing Tomorrow’s Autonomous Robots Today admin Tue, 03/25/2025 - 21:18

Every year, people in California risk their lives battling wildfires, but in the future, machines powered by artificial intelligence will be on the front lines, not firefighters.

However, this new generation of self-thinking robots will need security protocols to ensure they aren’t susceptible to hackers. To integrate such robots into society, they must come with assurances that they will behave safely around humans.

It begs the question: can you guarantee the safety of something that doesn’t exist yet? It’s something Assistant Professor Glen Chou hopes to accomplish by developing algorithms that will enable autonomous systems to learn and adapt while acting with safety and security assurances.

He plans to launch research initiatives, in collaboration with the School of Cybersecurity and Privacy and the Daniel Guggenheim School of Aerospace Engineering, to secure this new technological frontier as it develops.

“To operate in uncertain real-world environments, robots and other autonomous systems need to leverage and adapt a complex network of perception and control algorithms to turn sensor data into actions,” he said. “To obtain realistic assurances, we must do a joint safety and security analysis on these sensors and algorithms simultaneously, rather than one at a time.”

This end-to-end method would proactively look for flaws in the robot’s systems rather than wait for them to be exploited. This would lead to intrinsically robust robotic systems that can recover from failures.

Chou said this research will be helpful in other domains, including advanced space exploration. If a space rover is sent to one of Saturn’s moons, for example, it needs to be able to act and think independently of scientists on Earth.

Aside from fighting fires and exploring space, this technology could perform maintenance in nuclear reactors, automatically maintain the power grid, and make autonomous surgery safer. It could also bring assistive robots into the home, enabling higher standards of care.

This is a challenging domain where safety, security, and privacy concerns are paramount due to frequent, close contact with humans.

This will start in the newly established Trustworthy Robotics Lab at Georgia Tech, which Chou directs. He and his Ph.D. students will design principled algorithms that enable general-purpose robots and autonomous systems to operate capably, safely, and securely with humans while remaining resilient to real-world failures and uncertainty.

Chou earned dual bachelor’s degrees in electrical engineering and computer sciences as well as mechanical engineering from the University of California, Berkeley, in 2017, a master’s and Ph.D. in electrical and computer engineering from the University of Michigan in 2019 and 2022, respectively.

He was a postdoc at the Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory before joining Georgia Tech in November 2024. He received the National Defense Science and Engineering Graduate fellowship program, NSF Graduate Research fellowships, and was named a Robotics: Science and Systems Pioneer in 2022.

Summary sentence

The Trustworthy Robotics Lab enables robots and autonomous systems to operate safely with humans while remaining resilient to real-world challenges.

Summary

The Trustworthy Robotics Lab is a new interdisciplinary venture led by School of Cybersecurity & Privacy Assistant Professor Glen Chou. The lab's mission is to enable robots and autonomous systems to operate safely with humans while remaining resilient to real-world challenges.

Dateline

Tue, 03/04/2025 - 12:00

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J.P. Popham, Communications Officer

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Tue, 03/25/2025 - 21:18

Ph.D. Student Wins Best Paper at Robotics Conference

dgivens8 — Thu, 06 Jun 2024 14:35:37 +0000

Ph.D. Student Wins Best Paper at Robotics Conference dgivens8 Thu, 06/06/2024 - 10:35

Ask a person to find a frying pan, and they will most likely go to the kitchen. Ask a robot to do the same, and you may get numerous responses, depending on how the robot is trained.

Since humans often associate objects in a home with the room they are in, Naoki Yokoyama thinks robots that navigate human environments to perform assistive tasks should mimic that reasoning.

Roboticists have employed natural language models to help robots mimic human reasoning over the past few years. However, Yokoyama, a Ph.D. student in robotics, said these models create a “bottleneck” that prevents agents from picking up on visual cues such as room type, size, décor, and lighting.

Yokoyama presented a new framework for semantic reasoning at the Institute of Electrical and Electronic Engineers (IEEE) International Conference on Robotics and Automation (ICRA) last month in Yokohama, Japan. ICRA is the world’s largest robotics conference.

Yokoyama earned a best paper award in the Cognitive Robotics category with his Vision-Language Frontier Maps (VLFM) proposal.

Assistant Professor Sehoon Ha and Associate Professor Dhruv Batra from the School of Interactive Computing advised Yokoyama on the paper. Yokoyama authored the paper while interning at the Boston Dynamics’ AI Institute.

“I think the cognitive robotic category represents a significant portion of submissions to ICRA nowadays,” said Yokoyama, whose family is from Japan. “I’m grateful that our work is being recognized among the best in this field.”

Instead of natural language models, Yokoyama used a renowned vision-language model called BLIP-2 and tested it on a Boston Dynamics “Spot” robot in home and office environments.

“We rely on models that have been trained on vast amounts of data collected from the web,” Yokoyama said. “That allows us to use models with common sense reasoning and world knowledge. It’s not limited to a typical robot learning environment.”

What is Blip-2?

BLIP-2 matches images to text by assigning a score that evaluates how well the user input text describes the content of an image. The model removes the need for the robot to use object detectors and language models.

Instead, the robot uses BLIP-2 to extract semantic values from RGB images with a text prompt that includes the target object.

BLIP-2 then teaches the robot to recognize the room type, distinguishing the living room from the bathroom and the kitchen. The robot learns to associate certain objects with specific rooms where it will likely find them.

From here, the robot creates a value map to determine the most likely locations for a target object, Yokoyama said.

Yokoyama said this is a step forward for intelligent home assistive robots, enabling users to find objects — like missing keys — in their homes without knowing an item’s location.

“If you’re looking for a pair of scissors, the robot can automatically figure out it should head to the kitchen or the office,” he said. “Even if the scissors are in an unusual place, it uses semantic reasoning to work through each room from most probable location to least likely.”

He added that the benefit of using a VLM instead of an object detector is that the robot will include visual cues in its reasoning.

“You can look at a room in an apartment, and there are so many things an object detector wouldn’t tell you about that room that would be informative,” he said. “You don’t want to limit yourself to a textual description or a list of object classes because you’re missing many semantic visual cues.”

While other VLMs exist, Yokoyama chose BLIP-2 because the model:

Accepts any text length and isn’t limited to a small set of objects or categories.
Allows the robot to be pre-trained on vast amounts of data collected from the internet.
Has proven results that enable accurate image-to-text matching.

Home, Office, and Beyond

Yokoyama also tested the Spot robot to navigate a more challenging office environment. Office spaces tend to be more homogenous and harder to distinguish from one another than rooms in a home.

“We showed a few cases in which the robot will still work,” Yokoyama said. “We tell it to find a microwave, and it searches for the kitchen. We tell it to find a potted plant, and it moves toward an area with windows because, based on what it knows from BLIP-2, that’s the most likely place to find the plant.”

Yokoyama said as VLM models continue to improve, so will robot navigation. The increase in the number of VLM models has caused robot navigation to steer away from traditional physical simulations.

“It shows how important it is to keep an eye on the work being done in computer vision and natural language processing for getting robots to perform tasks more efficiently,” he said. “The current research direction in robot learning is moving toward more intelligent and higher-level reasoning. These foundation models are going to play a key role in that.”

Top photo by Kevin Beasley/College of Computing.

Summary sentence

Yokoyama presented a new framework for semantic reasoning for robots at the IEEE International Conference on Robotics and Automation, where he won best paper in the Cognitive Robotics category.

Summary

Yokoyama earned a best paper award in the Cognitive Robotics category with his Vision-Language Frontier Maps (VLFM) proposal.

Dateline

Thu, 06/06/2024 - 12:00

ndeen6@gatech.edu

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Thu, 06/06/2024 - 10:34

Good Dog: LASSIE Spirit Learns to Walk on the Moon

admin — Thu, 04 Apr 2024 14:47:41 +0000

Good Dog: LASSIE Spirit Learns to Walk on the Moon admin Thu, 04/04/2024 - 10:47

This story by Landon Hall was first published in the USC Viterbi School of Engineering newsroom.

Georgia Tech alumna Feifei Qian (M.S. PHYS 2011, Ph.D. ECE 2015), an assistant professor of electrical and computer engineering at the USC Viterbi School of Engineering and School of Advanced Computing, leads the NASA LASSIE project alongside co-investigator Frances Rivera-Hernández, an assistant professor in the School of Earth and Atmospheric Sciences at Georgia Tech. Sharissa Thompson, a graduate student at Georgia Tech, is a student intern on the NASA Curiosity Rover project.

The Palmer Glacier on Oregon’s Mount Hood isn’t the Moon, but it’s a good place to practice.

Some 6,000 feet up the snow-capped mountain, located about 70 miles east of Portland, a multi-disciplinary team from the University of Southern California, Texas A&M University, Georgia Institute of Technology, Oregon State University, Temple University, the University of Pennsylvania, and NASA gathered to turn loose a four-legged robot named Spirit into the wild.

The team that included engineers, cognitive scientists, geoscientists and planetary scientists field-tested Spirit as part of the LASSIE Project: Legged Autonomous Surface Science in Analog Environments. Spirit covered a variety of challenging terrains, using his spindly metal legs to amble over, across and over around shifting dirt, slushy snow and boulders during five days of testing in summer 2023. Sometimes he expertly traversed the hillside, while at other moments he teetered and fell over. All part of the process to better understand the substrate properties and learn to better walk on these extreme terrains. The practice time Spirit logged produced data that will be used to train future robots for use on intergalactic surfaces, like Earth’s moon and perhaps planets in our solar system.

“A legged robot needs to be able to detect what is happening when it interacts with the ground underneath, and rapidly adjust its locomotion strategies accordingly,” says Feifei Qian, an assistant professor of electrical and computer engineering at the USC Viterbi School of Engineering and School of Advanced Computing, which is leading the project funded by NASA. “When the robot leg slips on ice or sinks into soft snow, it inspires us to look for new principles and strategies that can push the boundary of human knowledge and enable new technology. We learn and improve from the observed failures.”

Watch this 5-minute video produced for the team by documentary filmmaker Sean Grasso.

Spirit learns from every step.

“Similar to the way that when we walk on uneven surfaces as humans, we can sort of detect how the ground is shifting beneath our feet, a legged robot is capable of the exact same thing,” says Cristina Wilson, a cognitive scientist at Oregon State University.

The more machines the merrier

Qian’s group doesn’t intend to stop at just one robot, wandering the wilderness alone. She and her former colleagues at Penn, Cynthia Sung, Mark Yim, Daniel Koditschek, and Douglas Jerolmack, received a two-year $2 million grant from NASA they’re calling the TRUSSES Project: Temporarily, Robots Unite to Surmount Sandy Entrapments, Then Separate. They want to help the space agency put teams of robots on the Moon and have them work together on tasks. They would take the knowledge they came in with, and the data they collect on the mission, and communicate those details to each other.

“They would sense how the ground conditions are,” Qian says, “and then exchange that information with one another, and collectively form a map of locomotion risk estimation. The team of robots can then use this traversal risk map to inform their planetary explorations: ‘There is an extremely soft sand patch that might be high-risk for wheeled rovers. Come over here, this might be a safer area.’ ”

The robots in mind for this kind of work would be more than just Spirit: There would be a wheeled rover (great for payload and long distances), a Hexapedal robot (intermediate payload but better mobility than the wheeled), and dog-like ones like the rugged version of Spirit (highest mobility, shorter distances). And here’s the coolest part of that research, the part that sounds like something the Transformers would do. Or at least a team of castaways on “Survivor”: If one got in a jam, made immovable by loose dirt or a rock or a ravine, his bot-mates would arrive and link together and form a bridge, or a pyramid, to hoist their pal to safety. And then back to work.

“When they plan for the strategy to pull the robot up, they’ll decide what force to exert and what position the robot should go to, while also compiling the terrain information,” Qian says. “That’s the key idea of how to use these capabilities: to both prevent and recover from locomotion failures in extreme terrain.”

Back to Mount Hood

Spirit gets around a variety of natural environments, to learn how to better move on challenging terrains. Qian has let him off his leash on Southern California beaches, and the multi-university team has field-tested him in the soft granules of White Sands National Park in New Mexico. But the video shot at Mount Hood shows just how otherworldly that landscape can be in these planetary-analogue environments. This provides Spirit with plenty of opportunities to learn on earth, before potentially exploring other planets.

“You look around us, it would be very hard to drive up this,” Ryan Ewing, a geologist from NASA Johnson Space Center, shares. “But as a legged being, as humans, we can step around it easily. A dog could walk around it easily. So this project is the proving ground that we can enable new science and new mobility on environments that are like other planets.”

In fact, a dog is indeed frisking about: Howard, Wilson’s German shepherd, wandered about, with the kind of agility Spirit could only dream of.

“We are going to observe how Howard moves in different types of snow and ice conditions,” Qian says. “What exactly, out of those combined motions, allows him to succeed on challenging terrain?”

The LASSIE Project calls for two more trips for Spirit: to Mount Hood this summer, and to White Sands next year. The TRUSSES team, from USC and Penn, also plans to visit White Sands next year with Spirit and the other, new, multi-tasking robots. Imagine WALL-E with friends.

—

The NASA PSTAR (Planetary Science and Technology Through Analog Research) number for this project is 80NSSC22K1313.

Subtitle

Researchers at Georgia Tech have teamed up with NASA and five peer institutions to teach dog-like robots to navigate craters of the Moon and other challenging planetary surfaces.

Summary sentence

Researchers at Georgia Tech have teamed up with NASA and five peer institutions to teach dog-like robots to navigate craters of the Moon and other challenging planetary surfaces.

Summary

Scientists at Georgia Tech have teamed up with the University of Southern California (USC), University of Pennsylvania, Texas A&M, Oregon State, Temple University, and NASA Johnson Space Center to teach dog-like robots to navigate craters of the Moon and other challenging planetary surfaces in research funded by NASA.

Dateline

Wed, 04/03/2024 - 12:00

jess@cos.gatech.edu

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Jess Hunt-Ralston
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Thu, 04/04/2024 - 10:45

Universal Controller Could Push Robotic Prostheses, Exoskeletons Into Real-World Use

admin — Wed, 20 Mar 2024 18:09:41 +0000

Universal Controller Could Push Robotic Prostheses, Exoskeletons Into Real-World Use admin Wed, 03/20/2024 - 14:09

Robotic exoskeletons designed to help humans with walking or physically demanding work have been the stuff of sci-fi lore for decades. Remember Ellen Ripley in that Power Loader in Alien? Or the crazy mobile platform George McFly wore in 2015 in Back to the Future, Part II because he threw his back out?

Researchers are working on real-life robotic assistance that could protect workers from painful injuries and help stroke patients regain their mobility. So far, they have required extensive calibration and context-specific tuning, which keeps them largely limited to research labs.

Mechanical engineers at Georgia Tech may be on the verge of changing that, allowing exoskeleton technology to be deployed in homes, workplaces, and more.

A team of researchers in Aaron Young’s lab have developed a universal approach to controlling robotic exoskeletons that requires no training, no calibration, and no adjustments to complicated algorithms. Instead, users can don the “exo” and go.

Their system uses a kind of artificial intelligence called deep learning to autonomously adjust how the exoskeleton provides assistance, and they’ve shown it works seamlessly to support walking, standing, and climbing stairs or ramps. They described their “unified control framework” March 20 in Science Robotics.

“The goal was not just to provide control across different activities, but to create a single unified system. You don't have to press buttons to switch between modes or have some classifier algorithm that tries to predict that you're climbing stairs or walking,” said Young, associate professor in the George W. Woodruff School of Mechanical Engineering.

Get the full story on the College of Engineering website.

Summary sentence

Aaron Young’s team has developed a wear-and-go approach that requires no calibration or training.

Summary

Aaron Young’s team has developed a wear-and-go approach that requires no calibration or training.

Dateline

Wed, 03/20/2024 - 12:00

jstewart@gatech.edu

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Joshua Stewart
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Wed, 03/20/2024 - 14:09

How Insects Evolved to Ultrafast Flight (And Back)

admin — Mon, 09 Oct 2023 19:50:41 +0000

How Insects Evolved to Ultrafast Flight (And Back) admin Mon, 10/09/2023 - 15:50

Mosquitoes are some of the fastest-flying insects. Flapping their wings more than 800 times a second, they achieve their speed because the muscles in their wings can flap faster than their nervous system can tell them to beat.

This asynchronous beating comes from how the flight muscles interact with the physics of the insect’s springy exoskeleton. This decoupling of neural commands and muscle contractions is common in only four distinct insect groups.

For years, scientists assumed these four groups evolved these ultrafast wingbeats separately, but research from the Georgia Institute of Technology and the University of California, San Diego (UC San Diego) shows that they evolved from a single common ancestor. This discovery demonstrates evolution has repeatedly turned on and off this particular mode of flight. The researchers developed physics models and robotics to test how these transitions could occur.

Read the full feature in the GT Research newsroom.

Summary sentence

Many insects fly synchronously, matching the nervous system pulses to wing movement. But smaller insects don’t have the mechanics for this and must flap their wings harder, which works only up to a certain point. That’s where asynchronous flight comes in.

Summary

Dateline

Fri, 10/06/2023 - 12:00

jess@cos.gatech.edu

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Media Contact: Tess Malone | tess.malone@gatech.edu

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Mon, 10/09/2023 - 15:48

Novel Policy Allows Robots to Perform Interactive Tasks in Sequential Order

admin — Thu, 31 Aug 2023 15:26:31 +0000

Novel Policy Allows Robots to Perform Interactive Tasks in Sequential Order admin Thu, 08/31/2023 - 11:26

Georgia Tech Ph.D. student Niranjan Kumar created the Cascaded Compositional Residual Learning (CCRL) framework, enabling a quadrupedal robot to perform increasingly complex tasks without relearning motions, mirroring human learning, showcased by the robot opening a heavy door using energy transfer, a remarkable achievement in robotics.

The CCRL, however, functions as a “library” that allows the robot to remember everything it has learned while performing the simple tasks. Each newly obtained skill is added to the library and leveraged for more complex skills. A turning motion, for instance, can be learned on top of walking while serving as the basis for navigation skills.

Kumar said CCRL has broken new ground on interactive navigation research. Interactive navigation is one of several navigation solutions that allow robots to navigate in the real world. These solutions include point navigation, which trains a robot to reach a point on a map, and object navigation, which teaches it to reach a selected object.

Interactive navigation requires a robot to reach a goal location while interacting with obstacles on the way, which has proven to be the most difficult for robots to learn.

The key, Kumar said, to get a robot to go from walking to pushing an object is in the joints and the robot discovering the different types of motions it can make with them.

So far, Kumar’s policy has reached 10 skills that a robot can learn and deploy. The number of skills it can learn on one policy depends on the hardware the programmer is using.

“It just takes longer to train as you keep adding more skills because now the policy also has to figure out how to incorporate all these skills in different situations,” he said. “But theoretically, you can keep adding more skills indefinitely as long as you have a powerful enough computer to run the policies.”

Kumar said he sees CCRL being useful for home assistant robots, which are required to be agile and limber to navigate around a cluttered household. He also said it could possibly serve as a guide dog for the visually impaired.

“If you have obstacles in front of someone who is visually impaired, the robot can just clear up the obstacles as the person is walking, open the door for them, and things like that,” he said.

Summary sentence

A Georgia Tech Ph.D. student has created a new framework that enables a four-legged robot to perform increasingly complex tasks without relearning motions.

Summary

Dateline

Fri, 08/18/2023 - 12:00

nathan.deen@cc.gatech.edu

Contact

Nathan Deen

Communications Officer I

School of Interactive Computing

nathan.deen@cc.gatech.edu

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go-researchnews

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Science and Technology

Categories

Robotics

Mercury ID

669031

Source updated

Thu, 08/31/2023 - 11:26

Back-to-Back Drone Racing Titles Further RotorJackets Goal of Expansion

admin — Fri, 05 May 2023 21:11:31 +0000

Back-to-Back Drone Racing Titles Further RotorJackets Goal of Expansion admin Fri, 05/05/2023 - 17:11

April was a month of national championships at Georgia Tech as Buzz was crowned the nation's best mascot, and the RotorJackets took home their second straight Collegiate Drone Racing championship.

Tech's team, comprised of four racers, narrowly emerged from the field of 15 schools and 52 individual pilots to take home the title following two days of competition at the Fayette Fliers Field in Tyrone, Georgia. While scores from the qualifying round and the previous races in the knockout round are compiled into a sum, it was RotorJackets' vice president Tanner Beard who put his team in the lead in the final race of the competition. Beard also finished in second place in the individual competition, but it's the team’s accomplishment that he's proudest of.

Beard and outgoing team president Luke Lawver started flying first-person view (FPV) drones together in 2018 before officially founding RotorJackets in the fall of 2020. As Beard, a mechanical engineering student, and Lawver, an aerospace graduate student, get set to leave Tech, the pair couldn't have imagined the success the group has achieved in a short time.

"I'm just very proud of how far it's come. We started out practicing on fields with PVC pipes, and our gates and materials were falling apart. We didn't really have anything, but we were able to build everything up, and we've practiced every single weekend for events like this," Beard said.

The team learned the layout of the championship track just two weeks before the event, but practicing from first light to sundown was nothing new for the RotorJackets. The hard work and preparation continued up until the last minute as the team was forced to replace a drone the night before the competition. But the two senior members of the team were impressed by first-year computer engineering student Ian Boraks –– the incoming president –– and Dylan Wyckoff, in his first-ever drone race. Wyckoff is a third-year computer science major and will take over as the club’s treasurer.

Both relished the opportunity to fly alongside their experienced teammates and are now focused on continuing their legacy in pursuit of a three-peat.

Other than winning titles, the club also wants to expand the drone-flying community on campus. When the fall semester begins, monthly events will be held on Tech Green, where all students can learn how to fly. No experience is necessary, and all equipment will be provided. Not all club members are racers, and the benefits of learning the skill go well beyond the group.

"The cool thing about FPV as a hobby is that, especially as an engineering major or someone in any STEM field, it teaches you a ton of practical skills that are incredibly useful in your day-to-day job," Lawver said. "We build all of our drones basically from scratch, so you can learn about electrical hardware design, mechanical hardware design, and software engineering and dive into whatever areas you want. Or, you can just treat it as a black box, and you'll have fun with it."

Using the skills they've acquired, the RotorJackets have expanded their footprint at Tech, using drones to enhance coverage of events like the iconic Mini-500 as well the Pi Mile, and they've assisted Athletics in creating digital content.

While not necessary to join the club and learn the ropes, obtaining your Remote Pilot Certificate from the FAA is recommended if you're interested in the more advanced stages of drone piloting, and the plan is to offer this certification through the club in the coming semesters.

For information on how to join the RotorJackets and the latest updates on upcoming events, join their Discord.

Summary sentence

RotorJackets, Georgia Tech’s drone racing team, captured their second straight national title, but their mission extends beyond winning championships.

Summary

RotorJackets, Georgia Tech’s drone racing team, captured their second straight national title, but their mission extends beyond winning championships.

Dateline

Fri, 05/05/2023 - 12:00

Steven.gagliano@gatech.edu

Contact

Steven Gagliano - Communications Officer

Institute Communications

Associated importer

News room topics

Campus and Community

Science and Technology

Categories

Aerospace

Student and Faculty

Engineering

Robotics

Institute and Campus

Mercury ID

667662

Source updated

Fri, 05/05/2023 - 17:07

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