Robotics
Robotics

Robotics

Aeronautical and astronautical engineering Algorithms Artificial Intelligence Autonomous vehicles Computer science and technology Drones Electrical engineering and computer science (EECS) IDSS Laboratory for Information and Decision Systems (LIDS) machine learning Mechanical engineering MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research Robotics School of Engineering

AI-enabled control system helps autonomous drones stay on target in uncertain environments

The system automatically learns to adapt to unknown disturbances such as gusting winds.

Artificial Intelligence Augmented and virtual reality Center for Bits and Atoms Computer science and technology Design DesignX Digital technology Electrical engineering and computer science (EECS) Graduate, postdoctoral Human-computer interaction Innovation and Entrepreneurship (I&E) MIT Morningside Academy for Design MIT Schwarzman College of Computing Natural language processing Profile Robotics School of Architecture and Planning School of Engineering Students Technology and society

Merging design and computer science in creative ways

MAD Fellow Alexander Htet Kyaw connects humans, machines, and the physical world using AI and augmented reality.

Aeronautical and astronautical engineering Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Human-computer interaction machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

Robotic helper making mistakes? Just nudge it in the right direction

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

Algorithms Artificial Intelligence Computer science and technology Games machine learning Media Lab Research Robotics School of Architecture and Planning

New training approach could help AI agents perform better in uncertain conditions

Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.

Aeronautical and astronautical engineering Algorithms Artificial Intelligence Automation Autonomous vehicles computer-vision Drones Faculty Laboratory for Information and Decision Systems (LIDS) MIT Schwarzman College of Computing Profile Robotics School of Engineering Software

Expanding robot perception

Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Natural language processing Quest for Intelligence Research Robotics School of Engineering

Teaching a robot its limits, to complete open-ended tasks safely

The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.

Algorithms Artificial Intelligence Autonomous vehicles Awards, honors and fellowships Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Faculty Human-computer interaction MIT Schwarzman College of Computing Robotics School of Engineering

Daniela Rus wins John Scott Award

MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Data Electrical engineering and computer science (EECS) Human-computer interaction machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

Can robots learn from machine dreams?

MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

A faster, better way to train general-purpose robots

Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering Video

Combining next-token prediction and video diffusion in computer vision and robotics

A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.