< From left: Professor Myung Hyun, Ph.D. candidate Park Juhye, Master >
A robot that can follow a single verbal instruction, navigate an unfamiliar building, and verify that it has reached the correct destination has earned top honors in two international competitions.
KAIST (President Chungsik Bae) announced on September 11 that a team led by Professor Hyun Myung of the Urban Robotics Lab in the School of Electrical Engineering took first place in an international challenge held in conjunction with ECCV 2026, a leading computer vision conference, and second place in a challenge at RSS 2026, a leading robotics conference.
The team's technology goes beyond conventional navigation. It enables robots to interpret their surroundings and independently verify whether they have made the right decision.
Both competitions focused on embodied AI—the ability of a robot to understand human language, perceive its surroundings through cameras, make decisions, and act on them. Unlike AI systems that operate solely in the digital realm, embodied AI interacts directly with the physical world.
Professor Myung's research team joined forces with the Field Robotics Research Section of the Electronics and Telecommunications Research Institute (ETRI) to form the "URL-FRRS" team. The team placed first among 112 teams worldwide in the VLNVerse Challenge, a vision-language navigation competition held at the ECCV 2026 EMR Workshop in Malmö, Sweden on September 9.
The competition tested whether a robot could reach the correct destination based solely on a stated goal, such as "Find the blue sofa in the living room," or a multistep instruction, such as "Turn right at the end of the hallway and enter the second door."
When the research team analyzed why existing robots were failing, they found that, surprisingly, the more common cause was not an inability to find the route, but the robot mistakenly concluding it had "arrived" at the wrong location. A robot might, for example, pass the actual destination or mistake a similar-looking room for the target.
To address this problem, the team developed CoRe-VLN, short for Coverage-based Recovery for Vision-Language Navigation. The technology prompts the robot to verify its location whenever it is about to stop, essentially asking itself, "Is this really the destination?"
Once the robot determines that it may have reached its destination, it scans its surroundings again. An AI model capable of interpreting both text and images then checks whether the robot is in the correct room, whether the target object matches the specified color and shape, and whether the object is actually nearby. The process is similar to a person checking a sign or looking around after arriving at a meeting point to make sure they are in the right place.
If the robot determines that it has stopped at the wrong location, it plans a new route and resumes its search. Because CoRe-VLN can be added to an existing navigation system without further training, it gives the robot a self-verification and recovery capability that can be readily applied in unfamiliar environments.
The team achieved an average success rate of 90.7% across the two competition tasks. Although it tied with the runner-up in success rate, it secured first place by submitting its results earlier. The third-place team recorded a success rate of 89.7%.
The KAIST team also took second place in the NaviTrace Challenge at the RSS 2026 OWN Workshop, held in Sydney, Australia, in July.
In this competition, a robot was given only a single photograph and a brief command, such as "Drive into the parking lot," and had to determine the correct direction of travel.
When entering a parking lot, people instinctively distinguish the entrance from the exit by interpreting road markings and observing the direction of other vehicles. These cues, however, are not explicitly stated in the command. To navigate spaces shared with people, robots must be able to infer such implicit rules directly from the scene before them.
< Final rankings of the VLNVerse Challenge 2026 >
The team addressed this challenge by developing PRISM-Nav, a system in which multiple AI agents divide the work instead of relying on a single AI model to make every decision.
One agent locates the destination, another identifies hazards such as stairs and curbs, and a third recognizes socially significant features such as sidewalks and crosswalks. A final agent integrates their findings and determines the direction of travel. The approach resembles a group where one person checks the route, another watches for hazards, and a third interprets traffic rules before they jointly choose the safest path.
The researchers also streamlined communication among the AI agents. Instead of describing locations through lengthy text or numerical coordinates, the agents mark destinations and hazardous areas directly on an image and share the annotated image with one another, much like handing someone a marked-up map rather than explaining the route verbally. PRISM-Nav can also be deployed in new environments without retraining the AI models.
The team scored 53 points in the competition, finishing second behind a joint team from Nanjing University and FiveAges.
Notably, the team used multiple instances of the relatively inexpensive and lightweight Gemini 3 Flash model to outperform Gemini 3.1 Pro Preview, the more powerful model used by the organizers as a benchmark. The result demonstrated that coordinating several lightweight AI models can deliver better performance and cost efficiency than relying on a single, more expensive model to handle every task.
< Example results from PRISM-Nav >
"We are delighted that the potential of our technology has been recognized in a competition with research teams from around the world. This achievement demonstrates that AI can make decisions and act independently in unfamiliar environments without additional training." said Professor Myung.
He added, "We plan to extend this approach beyond navigation to tasks such as identifying, picking up, and moving objects, ultimately developing robots that can verify and correct their own decisions."
As the technology advances, robots may move beyond simply executing human commands. Before acting, they could independently ask, "Is this the right place?", "Is this the right object?", or "Is this action safe?" Such capabilities could benefit not only delivery and guide robots but also robots operating alongside people in logistics centers, factories, hospitals, and other shared environments.
< Certificate of the 2nd place award in the RSS 2026 NaviTrace Challenge >
Professor Myung's laboratory also took first place in international challenges held at ICRA 2026, a leading robotics conference, and CVPR 2026, a leading computer vision conference, in June. The laboratory is now expanding its expertise in spatial cognition into the field of physical AI.
This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP). (END)
