The Breakdown: RIO lets researchers use the same software across different robots, reducing the need to rebuild code for each new platform. This functionality speeds up robot setup and lets researchers spend more time developing and testing robot behaviors. The system helps accelerate robotics research and real-world deployment. * * * Researchers set up [...]
The Breakdown: VideoManip teaches robots manipulation skills using videos of people interacting with objects. It reconstructs movements and estimates how people make contact with objects. The system helps robots learn new skills without time-consuming, human-operated demonstrations. * * * Researchers in Carnegie Mellon University's School of Computer Science are developing a new way for robots [...]
A research group at Carnegie Mellon University’s Robotics Institute (RI) will host the latest phase of the Vision-Language-Navigation (VLN) Challenge, bringing researchers together to enable robots to understand and act on human instructions in the real world — one of robotics’ most famously difficult pursuits. This year’s challenge pushes beyond earlier versions by removing “ground [...]
The Breakdown Sim2Reason trains AI in simulated physics environments instead of using static text data. The system generates unlimited, high-quality training data from virtual worlds. The results improve real-world reasoning, including solving advanced physics problems. * * * Researchers in the Carnegie Mellon University School of Computer Science are rethinking how artificial intelligence learns about [...]
Our group is interested in inferring physically and spatially grounded representations from perceptual input, and leveraging these for advances in fundamental problems in computer vision and robot manipulation. We believe that to enable machines to understand the physical world, we should reduce the reliance on supervision by annotation, and instead develop learning mechanisms informed by [...]
Research includes real-time 3D Lidar SLAM, Lidar Odometry and Mapping (LOAM), navigation, planning, and scene understanding. More on Ji Zhang's page. Multi-robot Collaboration Multiple ground and aerial robots collaboratively explore and map, communicating and leveraging all sensing and mobility modalities across the team to maximize the coverage and efficiency by globally optimizing the plan. Efficient [...]
Our group studies Artificial Intelligence at the intersection of Computer Vision, Machine Learning & Robotics. Our ultimate goal is to build agents with a human-like ability to generalize in real and diverse environments. We believe understanding how to continually develop knowledge and acquire new skills from just raw sensory data will play a vital role [...]
The Momentum Lab focuses on advancing robotics research through innovative approaches to manipulation, learning, and human-robot interaction. Our research spans soft robotics, computer vision, machine learning, and multi-agent systems, with applications in healthcare, manufacturing, and everyday assistance.
At Generative Intelligence Lab, we are studying the collaboration between Human Creators and Generative Models, with the goal of building intelligent machines capable of helping everyone tell their visual stories. We are studying the following questions: Interaction between creators and generative models: How can we help creators control the model outputs more easily? We develop [...]
Research focuses on developing fundamental algorithms that enable large teams of autonomous agents to accomplish collaborative tasks intelligently in dynamic environments. Applications of this research include mobile robot coordination for automated warehousing and, in general, nonholonomic robot teams, drone swarm control, multi-arm assembly, character control in video games, and traffic management for airports, railway networks, [...]
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 [...]
From autonomous cars in cities to mobile manipulators at home, our lab aims to design robots that reliably interact with people. What makes this hard is that human behavior---especially when interacting with other agents---is vastly complex, varying between individuals, environments, and over time. Thus, robots rely on data and machine learning throughout the design process [...]
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 [...]
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 [...]
I am an Assistant Professor in the Robotics Institute at Carnegie Mellon University, leading the Interactive and Trustworthy Robotics Lab (Intent Lab). I'm broadly interested in enabling embodied AI agents to reliably interact with the open world. This includes---but is not limited to---quantifying a robot's confidence (e.g., in a requested task or in their predictive models [...]
I am interested in planning, scheduling, optimization, and learning for large-scale multi-robot coordination. My research focuses on developing fundamental algorithms that enable large teams of autonomous agents to accomplish collaborative tasks intelligently in dynamic environments. Applications of my research include mobile robot coordination for automated warehousing and, in general, non-holonomic robot teams, drone swarm control, [...]
My research broadly explores healthcare robotics, with an emphasis on empowering older adults and people with disabilities to live more independently and maintain a higher quality of life.My research lies at the intersection of physically assistive robotics, learning, physics simulation, haptic perception, and human-robot interaction.I am especially interested in physical human-robot interaction, and I focus on developing [...]
Our lab works to develop new methods for robot perception and control. We are focusing on deformable object manipulation, autonomous driving, and safe reinforcement learning.
At the Generative Intelligence Lab, we are studying the collaboration between Human Creators and Generative Models, with the goal of building intelligent machines capable of helping everyone tell their visual stories. We are studying the following questions: Interaction between creators and generative models: How can we help creators control the model outputs more easily? We [...]
My ultimate goal is to build agents with a human-like ability to generalize in real and diverse environments. I believe understanding how to continually develop knowledge and acquire new skills from just raw sensory data will play a vital role in achieving this goal. I draw inspiration from psychology to build practical systems at the [...]
The Intelligent Control Lab focuses on building intelligent and autonomous robots that think, behave and interact with the world in the way that human beings do so that they can better serve, assist and collaborate with people in their daily lives across work, home and leisure. We are part of the Robotics Institute at Carnegie [...]
My research interests is in reinforcement learning and deep neural networks. I'd like to develop an end to end algorithm for robot perception, control and actuation based on reinforcement deep neural networks.
My research lies at the intersection of robotics, machine learning, and computer vision. I am interested in developing methods for robotic perception and control that can allow robots to operate in the messy, cluttered environments of our daily lives. My approach is to design new deep learning / machine learning algorithms to understand environmental changes: [...]
My research interests lie in the area of AI in robotics including autonomy, language understanding, multimodal perception, path planning, and machine learning. I am interested in creating persistent robots that can co-exist with humans in shared environments, learning to improve themselves over time through continuous training, exploration, and interactions. Toward this general goal, I am [...]
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 [...]
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 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 [...]
Problems of large-scale coordination and logistics are ubiquitous, and better solutions are becoming increasingly critical in many domains. In manufacturing, trends toward industrial globalization and constrained market focus on high value-adding products, together with new coordination concepts such as electronic marketplaces, require organizations to become more agile. Military command and control infra-structure is faced with [...]
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 [...]
Zakia Hammal is an Assistant Research Professor with the Robotics Institute in the School of Computer Science at Carnegie Mellon University. Dr. Hammal’s areas of expertise are multimodal human behavior modeling in social interaction, health informatics, and affective computing. Much of her recent work has addressed computational models for multimodal assessment of treatment outcomes in [...]
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.
Research interests: computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, deep learning, assistive technologies for people with visual impairment
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 [...]
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 [...]
How do we represent the visual world? My research focuses on developing representation and reasoning approaches for deeper understanding of the scene. I am interested in formulating the scene understanding problem in terms of the underlying 3D scene and develop reasoning approaches based on physical, functional and causal relationships between the different elements in the [...]
