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To a robot, a glass wall looks like an open path. UMD researchers are using light polarization to help autonomous systems spot invisible barriers like glass—a project that earned 2 of 5 workshop honors captured by UMD’s Perception and Robotics Group @prgumd at IROS, a flagship international robotics conference.
Congrats to computer science Ph.D. students Naitri Rajyaguru @naitri_r, Zhi “Leo” Wang @TX_Leo_Wang and Amir-Hossein Shahidzadeh, along with their collaborators, including group co-directors Yiannis Aloimonos and Cornelia Fermüller. Read more: go.umd.edu/2g93View quoted post on X
👏 Congrats to Eadom Dessalene and Levi Burner who have earned the prestigious Intelligence Community Postdoctoral Research Fellowships to advance AI research in human-machine understanding and atmospheric turbulence prediction.
Read more: go.umd.edu/ICpostdocAward…View quoted post on X
What if teaching a robot a new task was as simple as showing it a video? Thanks to a new artificial intelligence framework developed by @UMDscience and @umiacs researchers, that’s now a possibility.
The system, called HumanEgo, requires no robot demonstrations, no robot-specific training data and no large-scale pretraining. Instead, the robotic systems can acquire new manipulation skills from as little as 30 minutes of first-person videos showing people performing tasks.
The research addresses one of robotics’ most persistent challenges, known as the embodiment gap. It describes the fundamental differences between human bodies and robots—how they look, move and perceive the world—that make it difficult to translate human actions into robotic behaviors.
today.umd.edu/umd-researcher…View quoted post on X
