The Breakdown Malfunctioning humanoids can hurt people, and improved safety measures are essential as these robots become more common in daily life. CMU and Siemens partnered to create a method to determine the "safe stoppability" of humanoids. They also defined conditions where it's safe to stop a humanoid using simulation, resulting in Proactive Refinement of [...]
The Breakdown: LAMP helps multiple robots collaborate to move objects. The system enables the robots to navigate crowded spaces and coordinate their movements. It could allow for more efficient workflows in warehouses and other complex environments. * * * Jiaoyang Li (left) and Shuai Zhou (right) are developing LAMP, a system that helps teams [...]
The Breakdown: World2Rules learns patterns of risky aircraft behavior from real airport operations and incident data. It explains its warnings in simple terms that are easy for humans to understand. World2Rules is designed to work alongside existing prediction systems to enhance aircraft safety. * * * When managing airport traffic, small errors can lead to [...]
The Breakdown: Super Odometry helps robots keep their sense of direction when smoke, darkness or dust block their cameras. The system allows robots to rely on internal motion sensors when they can’t see clearly. In real-world tests, it kept robots moving safely through tough conditions like stairs, low light and smoke. *** When a drone [...]
The Breakdown The BFM-Zero system lets humanoid robots switch seamlessly between many skills. It relies on unsupervised reinforcement learning, allowing robots to explore, generalize and self-improve. The system moves robotics closer to general-purpose humanoids that can adapt safely in everyday environments. *** Humanoid robots in a lab at Carnegie Mellon University don't fall over when [...]
The quadruped robots explore grass terrain at night. Team Chiron, a group of researchers from Carnegie Mellon University and the University of Pittsburgh, will compete in the third and final phase of the DARPA Triage Challenge (DTC) next November. The DTC seeks to develop portable, autonomous systems that can assess and monitor injured [...]
The Breakdown: Researchers developed a model that converts data into precise 3D maps. Trained on real-world scenes, the model captures both small details and large spaces with high precision. The research moves robotics closer to human-like spatial reasoning. *** Researchers from the Carnegie Mellon University Robotics Institute (RI) and Meta Reality Labs have built a [...]
Every day, the U.S. Federal Aviation Administration (FAA) handles tens of thousands of flights. Airspace traffic not only involves commercial planes, but helicopters, experimental lightcraft, freight carriers and an increasing number of uncrewed aerial systems (UAS). As air traffic density increases, so does the need for highly precise collision avoidance and traffic management systems to [...]
Appleseed Labs, a team within the Kantor Lab at Carnegie Mellon University, received the Excellence in Regenerative Agriculture award at the 2025 Farm Robotics Challenge. The team was advised by Senior Project Scientist Francisco Yandun and included students Will Heitman, Joyce Zhu, Ken Muangsiri, Alex Smith, Qimeng Yu and Rohan Walia, who joined from the [...]
Vivian Shen, Robotics Institute Ph.D. student, emerged as the champion of the Carnegie Mellon University Three Minute Thesis competition (3MT). The internationally recognized competition, hosted by Carnegie Mellon Libraries, challenges Ph.D. students from across the university to present their thesis in an accessible language in just three minutes. 3MT offers CMU doctoral candidates of any [...]
On Friday, March 7, the Textiles Lab at the Carnegie Mellon Robotics Institute, led by Associate Professor Jim McCann hosted a “Hard Textiles” lab jam. The lab jams show the unique ways textiles and technology interact, showcase the possible directions of future research and give the campus community an opportunity to see inside the lab [...]
The framework allows the robot to create photorealistic scene representations in low-light environments. Robots lift heavy loads in warehouses, deliver meals to diners, and even tackle housework. However, a lot of robotic work goes unseen – quite literally. Robots and autonomous vehicles are routinely deployed for critical tasks such as exploration, inspection, transportation, [...]
My life goal is to fulfill the science fiction vision of machines that achieve human levels of competence in perceiving, thinking, and acting. A more narrow technical goal is to understand how to get machines to generate and perceive human behavior. I use two complementary approaches, exploring humanoid robotics and human aware environments. Building humanoid [...]
Cognitive Assistance is a new research agenda to assist everyday life by fully utilizing the power of AI. AI is now starting to understand the surrounding world to make sense of the information for people who are unable to see. Additionally, those computers are starting to remember everything to fulfill the declining cognitive abilities of [...]
LeCar Lab members are broadly interested in the intersection of learning and control, spanning the entire spectrum from theory and foundations, algorithms, to real-world applications in robotics and autonomy. We develop reliable, adaptive, and efficient learning and control methods for embodied intelligence with agility. See our publications and research topics for more details. Videos and [...]
AIIRA’s vision is to create new AI-driven, predictive digital twins for modeling plants, and deploy them to increase the resiliency of the nation’s agricultural systems. AIIRA's institutions cover three of the five major areas of the US mainland, including Midwest, Southwest, and Northeast with stakeholders composed of companies, governmental agencies, commodity groups, prominent centers, and [...]
The goal of the FARM Lab (formerly Kantor Lab) is to develop techniques for perceiving and manipulating plants using robots. The potential applications are broad, and include fruit detection and picking, yield prediction, health assessment, pruning, and crop management, among others. The lab currently has projects spanning outdoor interactions with field crops, indoor work with [...]
My research interests are in the intersection of machine learning and control theory, spanning the entire spectrum from theory and foundations, and practical algorithms, to real-world applications in robotics and autonomy. To that end, there are three goals: (1) bridge learning and control theory in a unified framework; (2) design reliable learning and control algorithms [...]
The DROP (Deep Robot Optical Perception) Lab's research goal is to develop robotic systems capable of operating in complex dynamic environments. To this end, we seek to expand and improve the perceptual capabilities of autonomous systems. Our work seeks to push the bounds of scale and resolution in 3D reconstruction, segmentation, data mining, and visualization [...]
For more information, please visit Laszlo Jeni's CUBE Lab Site.
Group for research in computational imaging, illumination, display, light transport, microscopy, and more. For a full list of people from departments outside of the Robotics Institute, please visit the team page from the group website.
Carnegie Mellon has been a pioneering leader in autonomous transportation and self-driving technologies. The collaboration between CMU and Argo AI will continue to advance important research that will address the consequences of scaling technological innovation to society at large. This center will focus on fundamental research that will produce advanced perception and next-generation decision-making algorithms that enable vehicles to [...]
The Resilient Intelligent Systems Lab (RISLab) is part of Carnegie Mellon University’s Robotics Institute. The lab, led by Director Wennie Tabib, is focused on resilient autonomy and agile flight in complex, unstructured environments. https://www.rislab.org
Research Topics Computer Vision Light-weight Deep Learning Visual Perception on Moving Platforms Wireless and Wearable Sensing Applications Computer Vision Surveillance & Security Field Robotics Health Care Advances in sensors, machine learning and computer vision hold tremendous potential to reshape our world. DeLight focuses on unlocking this potential contained in applied machine learning approaches, and applying [...]
The Robot Perception Lab performs research related to localization, mapping and state estimation for autonomous mobile robots. The lab was founded in 2014 by Prof. Michael Kaess. The lab is part of the Robotics Institute at Carnegie Mellon University and belongs to both the Field Robotics Center and the Computer Vision Group.
I am an Assistant Professor with the Robotics Institute and Computer Science Department in the School of Computer Science at Carnegie Mellon University. My research interests span many topics across computer graphics and computer vision, but I'm particularly interested in computational imaging: a field that combines optics, electronics, and computational processing to capture or display [...]
My research focuses on developing algorithms and representations to enable robots to learn versatile manipulation skills over time. The ability to learn skills and adapt manipulations to new situations will open up a wide range of new robot applications, including taking care of the elderly, maintaining parks and public places, and assisting in hazardous environments. [...]
My research interest is in end-to-end robotic systems tailored for complex outdoor environments, synthesizing cutting-edge research across various branches of robotics to address real-world challenges. This often leads to proof-of-concept robotic systems that demonstrate novel concepts to do things that have never been done before. My particular interest is in agriculture, where I'm fascinated not [...]
The community of researchers doing Computer Vision @ CMU.
Search-based Planning Laboratory researches methodologies and algorithms that enable autonomous systems to act fast, intelligently and robustly. Our research concentrates mostly on developing novel planning approaches, coming up with novel heuristic searches and investigating how planning can be combined with machine learning. Our work spans graph theory, algorithms, data structures, machine learning and of course [...]
The Illumination and Imaging (ILIM) Laboratory at the Robotics Institute is dedicated to the study of light transport and the development of novel illumination and imaging technologies. The laboratory is part of the broader computer vision and computer graphics groups at Carnegie Mellon. Our research is motivated by applications in the areas of digital imaging, [...]
The goal of the Human Sensing Lab is to develop new machine learning algorithms to model and understand human behavior from sensory data (e.g. video, motion capture, audio). Our work is motivated by applications in the fields of human health, biometrics and human-machine interface. For more information, visit the full lab site.
The Visualization and Image Analysis (VIA) Laboratory is based at the University of Pittsburgh and Carnegie Mellon University. We are developing new methods of displaying, analyzing, and haptically rendering images, primarily for medical applications. We have introduced a new device called the Sonic FlashlightTM, for guiding invasive medical procedures, and are also involved in creating [...]
The Auton Lab, part of Carnegie Mellon University's School of Computer Science, researches new approaches to Statistical Data Mining. It is directed by Artur Dubrawski, Andrew Moore and Jeff Schneider. We are very interested in the underlying computer science, mathematics, statistics and AI of detection and exploitation of patterns in data. We build practical large-scale [...]
Agent Interoperability: We are designing reusable agent types, architectures, and languages to address the problem of interagent communication, coordination and negotiation. Electronic Commerce: We are addressing the need for agent-based negotiation and decision techniques that can be applied directly to the challenges facing the electronic marketplace. Information Gathering: The rapid growth of electronic information threatens [...]
Please visit the official Computer Graphics Lab website for current information - thank you!
The Biomedical Image Guidance (BIG) Lab conducts research in biomedical optics, image analysis, and visualization, especially for intraoperative guidance and also for diagnosis. We use computer vision to assist human placement of biomedical scanners, and we use novel visualization of biomedical images to better guide human manipulation of tools. Applications are wide-ranging, from computer-vision for [...]
My research interests are in Computer Vision, Digital Humans, and Computational Behavior Science, specifically in areas of modeling, understanding, and synthesizing human motion and behavior using diverse sensors. Currently I am directing the CUBE Lab. The research in the Computational Behavior (CUBE) Lab broadly focuses on computer vision and machine learning for behavior science and [...]
Research Topics Computer Vision Light-weighted Deep Learning Visual Perception on Moving Platforms Wearable Human Sensing Applications Computer Vision Surveillance & Security Field Robotics Health Care See more details in my Research Projects Advances in sensors, machine learning and computer vision hold tremendous potential to reshape our world. My research focuses on unlocking this potential contained [...]
Software agents and robots ranging from autonomous vehicles to intelligent assistants have become commonplace. We envision a future where this trend accelerates leading to large numbers of networked humans and robots working together for numerous applications ranging from manufacturing to environment exploration, crisis response and search and rescue. These multi-robot systems will be coordinating autonomously, [...]
Professor of Bioengineering, University of Pittsburgh and Adjunct Research Professor, Robotics Institute, CMU. MD, State University of New York, Health Science Center at Syracuse, 1991; PhD (Biomedical Engineering), University of North Carolina at Chapel Hill, 1999. Dr. Stetten’s current research interests include image-guided surgery using an ultrasound device he invented called the Sonic Flashlight, and various adaptations [...]
My research is in active learning, data mining, reinforcement learning, optimization, and intelligent control. My efforts are focused on the application of these methods to real world industrial and commercial problems.
My research focuses on computer vision, often motivated by the task of understanding people from visual data. My work tends to make heavy use of machine learning techniques, often using the human visual system as inspiration. For example, temporal processing is a key component of human perception, but is still relatively unexploited in current visual [...]
My research focuses on the physics of computer vision and computer graphics. My projects highlight three main aspects of my research – the mathematical modeling of the interactions of light with materials and the atmosphere; the design of novel cameras and programmable lighting; and the development of algorithms for rendering and interpreting scene appearance. My [...]
My general research interests lie in Artificial Intelligence and Robotics. More specifically, they currently cover planning in deterministic and probabilistic domains and machine learning. My research has been mainly motivated by the problem of fast and intelligent decision making by autonomous robotic systems operating in real-world environments. Some of the robotic systems my group does [...]
Research interests: computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, deep learning, assistive technologies for people with visual impairment
The control of dynamical systems becomes increasingly important as the era of robotics research dominated by quasi-static machines rapidly comes to a close. Similarly, the importance of state estimation grows as robotic applications require robots to function in larger, more complex environments. My research addresses both of these issues by focusing on the dual problems [...]
Perception is a fundamental challenge for mobile robots navigating through and interacting with their environment. My research focuses on 3D mapping and localization using information from any available sensor, including vision, laser, inertial, GPS and sonar (underwater). To enable online operation, my research also explores novel algorithms for efficient and robust inference at the intersection [...]
Dr. De la Torre's research interests include machine learning, signal processing and computer vision, with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). I am particularly interested in three main topics: Component Analysis (CA): CA methods (e.g. kernel PCA, Normalized Cuts, Multidimensional Scaling) are a set of algebraic techniques that decompose [...]
My research focuses on the coordination and control of dynamic physical systems, both natural and human-made and explores techniques that may someday allow robots and animated creatures to plan and control their actions in complex and unpredictable environments while interacting with humans. To make progress toward this goal, I have active projects in computer graphics, [...]
I work broadly in computer vision and computer graphics, but I focus on computational imaging: this is the joint design of optics, electronics, and computation to create imaging systems with unprecedented capabilities. Some examples include: imaging systems that can see around corners or through skin; passive 3D sensing systems with extreme resolution; ultrafast programmable lenses; [...]
CV, Research Statement, and Teaching Statement I am a Systems Scientist and Adjunct Assistant Professor at Carnegie Mellon University (CMU), directing the Biomedical Image Guidance Laboratory and teaching an internationally recognized graduate course on medical image analysis algorithms. I have a Ph.D. in Robotics and a B.S. and M.S. in computer engineering. My primary appointment [...]
I am interested in intelligent systems that work, are useful, and make economic sense, and in finding ways to effectively build and deploy them. My work is driven by real-world applications, currently in the areas of public health, food safety, nuclear safety and health of equipment. It involves researching new machine learning algorithms and data [...]
My research goal is to create systems and methodologies that allow groups of robots and humans to collaborate with one another to perform useful tasks. My research is motivated by an interest in the modeling and control of physical systems on the one hand, and in the creation of effective human-machine interfaces on the other. [...]
