Martial Hebert
University Professor and Dean
- Pittsburgh PA UNITED STATES
Martial Hebert performed research on interpreting 3D data from range sensors for obstacle detection and object recognition.
Biography
Hebert's research primarily centers on computer vision. He has led research on fundamental components, such as scene understanding, object recognition and applying machine learning to computer vision, as well as applications, which include systems that enable older adults and people with disabilities to live more independently. To help meet the needs of a rapidly expanding computer vision industry, he created the nation's first master's degree program in computer vision.
As director of the Robotics Institute, Hebert led an institution with more than 800 community members, including faculty, students and staff and colleagues at the National Robotics Engineering Center. During his tenure, the institute's operating budget reached an all-time high.
Hebert is a member of both the IEEE Robotics and Automation, and the IEEE Computer societies. Throughout his career, he has published hundreds of refereed papers in journals and conference proceedings, and has contributed to multiple edited volumes. He currently serves as editor-in-chief of the International Journal of Computer Vision. In 2022 he was named a University Professor, CMU's highest distinction for faculty members.
Areas of Expertise
Media Appearances
Top developers are pivoting from chatbots to physical AI
Associated Press
2026-06-24
“There’s all the geometry of the world, the dynamic of how I move my hand, the physical interaction of the contact with the cup,” Hebert said. “This is much more complex than just predicting the next word in a sentence.”
For scientists like Hebert, who has spent more than four decades researching robotics, the most useful application for world models is as a faster and cheaper path to “physical AI” — another tech industry buzzword.
“Some people may have different definitions, but physical and embodied AI are kind of the evolution of what we used to call robotics,” Hebert said in an interview. Some of the AI advances that have made chatbots so useful can also be applied to building AI with a broad enough awareness of its environment to work like a robot’s brain, he said.
“In your body and spinal cord you have a very general model of how to balance, how to walk around, and you can adapt to your knee hurting in the morning, so you now walk a little differently,” he said. “You don’t need to think about that. You have a general model somewhere in your nervous system and brain that allows your body to adapt very quickly.”
Want an AI degree? Here’s what you should think about.
The Star online
2026-06-09
Martial Hebert, dean of Carnegie Mellon’s School of Computer Science, said both approaches could be valuable.
“You just need to know that’s what you’re getting,” he said.
Carnegie Mellon graduates its first student with a bachelor’s degree in robotics
Pittsburgh Post-Gazette online
2026-05-14
“This degree will equip students with the skills needed to steer technological advancements and confront real-world challenges with unwavering zeal and unparalleled ingenuity,” Martial Hebert, robotics professor and dean of the School of Computer Science, said when CMU launched the program. “Our graduates will be in demand for important roles in industry and highly desirable for further research opportunities.”
The bachelor of science in robotics degree was the fifth undergraduate degree offered by the university’s computer science school, joining artificial intelligence, computational biology, computer science and human-computer interaction.
What are programmable cloud labs and why do lawmakers want to fund a national network of them?
90.5 WESA - Pittsburgh's NPR News Station radio
2025-12-22
CMU professor of robotics and dean of the School of Computer Science Martial Hebert said the labs would offer more than just access to more data.
Shaping a cohesive AI strategy is a key focus of Pittsburgh summit
Post Gazette online
2025-09-07
The AI Horizons Summit is about boosting Pennsylvania's role as a physical AI epicenter. "In all of these industries, we must be able to trust the machines to work alongside humans. Physical AI is the answer to that," said Dean of the School of Computer Science, Martial Hebert.
Informatica and Carnegie Mellon University Partner to Drive Innovation in Generative AI for Data Management
Financial Times online
2025-04-17
CMU's School of Computer Science and Informatica are partnering to advance the development and application of generative AI technologies for data management. "By working with Informatica, we can explore real-world applications of generative AI while providing valuable research and hands-on learning opportunities for our students and faculty," said Dean Martial Herbert (School of Computer Science).
Computer vision researchers use motion to discover objects in videos
Tech Xplore online
2023-07-27
Martial Hebert, dean of CMU's School of Computer Science and a professor in the Robotics Institute, and robotics Ph.D. student Zhipeng Bao collaborated on the project with Toyota Research Institute, which sponsored the work. The research could help computers and robots better automatically detect objects in videos.
Back to the future: Look to the 1980s for guidance on AI management
The Hill online
2023-07-05
Many have called to slow down the advancement of large language models, the kind of artificial intelligence (AI) that powers ChatGPT. But when it comes to AI development, we need to act now.
Hebert, Hooker and Kraut Named University Professors
Carnegie Mellon University News online
2022-04-11
Three Carnegie Mellon University faculty members have been elevated to the rank of University Professor(opens in new window), the highest distinction a faculty member can achieve at CMU. The newly appointed University Professors are Martial Hebert, John Hooker and Robert E. Kraut.
Hebert Installed as SCS Dean
Carnegie Mellon University News online
2020-01-30
It's fitting that the installation of School of Computer Science Dean Martial Hebert would begin with a musical intro from Carnegie Mellon University's robot bagpiper, McBlare. After all, Hebert spent his career at the Robotics Institute until taking the helm of SCS this past August.
Media
Social
Industry Expertise
Education
University of Paris
License de Mathématiques
University of Paris
Maitrise de Mathématiques Appliquées
University of Paris
Doctorate
Computer Science
Affiliations
- IEEE Robotics and Automation Society : Member
- IEEE Computer Society : Member
- International Journal of Computer Vision : Editor-in-Chief
Links
Articles
Generative Modeling for Multi-task Visual Learning
Proceedings of the 39th International Conference on Machine Learning2022
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across various visual perception tasks.
Semantically supervised appearance decomposition for virtual staging from a single panorama
ACM Transactions on Graphics2022
We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations.
Approximate Differentiable Rendering with Algebraic Surfaces
European Conference on Computer Vision2022
Differentiable renderers provide a direct mathematical link between an object’s 3D representation and images of that object. In this work, we develop an approximate differentiable renderer for a compact, interpretable representation, which we call Fuzzy Metaballs. Our approximate renderer focuses on rendering shapes via depth maps and silhouettes. It sacrifices fidelity for utility, producing fast runtimes and high-quality gradient information that can be used to solve vision tasks.
Learning Continuous Implicit Representation for Near-Periodic Patterns
European Conference on Computer Vision2022
Near-Periodic Patterns (NPP) are ubiquitous in man-made scenes and are composed of tiled motifs with appearance differences caused by lighting, defects, or design elements. A good NPP representation is useful for many applications including image completion, segmentation, and geometric remapping.
Object Discovery From Motion-Guided Tokens
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2023
Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clustering of low-level cues, whether handcrafted (e.g., color, texture) or learned (e.g., from auto-encoders). In this work, we augment the auto-encoder representation learning framework with two key components: motion-guidance and mid-level feature tokenization.