Research

  • Impact Areas
  • Research Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & ML

Spanning natural language processing, deep-learning, computer vision and more

Computational Biology

Understanding disease via epigenomics, gene regulation and bioinformatics

Computer Architecture

How CPUs, memory and other systems are designed and organized

Graphics & Vision

Teaching computers to interpret, create and animate visual data

Human-Computer Interaction

Software and hardware that let us naturally interact with technology

Programming Languages & Software Engineering

From compilers and verification to software design and engineering

Robotics

Vision, actuation, sensing and manipulation of machines

Security & Cryptography

Developing technologies to prevent and recover from cyber-attacks

Systems & Networking

From distributed systems and databases to wireless

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8 Group Results

Research Center

Center for Deployable Machine Learning (CDML)

The MIT Center for Deployable Machine Learning (CDML) works towards creating AI systems that are robust, reliable and safe for real-world deployment.
Daskalakis
Moitra-headshot
Rinard
+5
Madry-headshot

Aleksander Madry

Leads

Madry-headshot

Aleksander Madry

Research Areas

Algorithms & Theory
AI & ML
Graphics & Vision
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Security & Cryptography

Impact Areas

Big Data
Cybersecurity

Lead

Madry-headshot

Aleksander Madry

Daskalakis
Moitra-headshot
Rinard
+5
 
Madry-headshot

Aleksander Madry

26 Project Results

Project

Active Learning of Models for Planning

Learning and Intelligent Systems
Machine Learning
We aim to develop a systematic framework for robots to build models of the world and to use these to make effective and safe choices of actions to take in complex scenarios.
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Leads

Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Research Areas

Algorithms & Theory
AI & ML
Human-Computer Interaction
Robotics

Impact Areas

Big Data
Education
Entertainment
 
Kaelbling

Leslie Kaelbling

Lozano-Perez

Tomas Lozano-Perez

Project

An Interlock for Self Driving Cars

Software Design Group
Computer-Aided Programming
Vertical AI Community of Research
Human-Computer Interaction Community of Research
Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Leads

Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Research Areas

AI & ML
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Systems & Networking

Impact Areas

Transportation
 
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Project

Artificial Social Intelligence for Successful Teams (ASIST)

Computation Structures Group
Computational Cognitive Science Group
Model-Based Embedded and Robotics Systems Group
Our goal is to develop a socially intelligent team coacher agent that helps humans communicate, strategize, and work together efficiently.

Leads

Howard Shrobe

Howard Shrobe

Williams

Brian Williams

Katz

Boris Katz

default headshot

Zhutian Yang

Research Areas

Computer Architecture
Human-Computer Interaction
Robotics

Impact Areas

Health Care
Transportation
Wireless

Project

Geordi: A Driver's Assistant for Risk-bounded Maneuvering

Model-Based Embedded and Robotics Systems Group
Cognitive AI Community of Research
Our goal is to create an online risk-aware planner for vehicle maneuvers that can make driving safer and less stressful through a “parallel” autonomous system that assists the driver by watching for risky situations, and by helping the driver take proactive, compensating actions before they become crises.
Williams

Brian Williams

Leads

Williams

Brian Williams

Research Areas

Robotics

Impact Areas

Transportation
 
Williams

Brian Williams

Project

Safety Standards for Autonomous Vehicles

Distributed Robotics Laboratory
In this project, we aim to develop a framework that can ensure and certify the safety of an autonomous vehicle. By leveraging research from the area of formal verification, this framework aims to assess the safety, i.e., free of collisions, of a broad class of autonomous car controllers/planners for a given traffic model.

Leads

Lucas_Pic

Lucas Liebenwein

default headshot

Wilko Schwarting

Rus

Daniela Rus

Research Areas

Algorithms & Theory
Robotics

Impact Areas

Transportation

17 People Results

Rus

PI
Core/Dual

Daniela Rus

Director
CSAIL director, robotics, AI, data science, privacy
Rus
T 258-7567
RM 32/ 32-368/ 374

Lead

Distributed Robotics Laboratory
Social Network Extraction from GPS Datasets with Coresets
Safety Standards for Autonomous Vehicles
Soft Contact Modeling
Practical Secure Computation
Self-Driving Vehicles
Robust Learning for Autonomous Vehicles
Brain-Controlled Robots
Planning in Congestion for Autonomous Vehicles
Socially-Compliant Behavior for Autonomous Driving
Coresets for Machine Learning Algorithms
M-Blocks Modular Robotics
Robot Compiler
Distributed Robot Garden
Route and Motion Planning for Mobility-on-Demand with Rules of the Road

Member

Social Network Extraction from GPS Datasets with Coresets
Task-Driven Co-Optimization Of Articulated Robots
Wearable Blind Navigation
Drones that Drive
Better Models for Ride-Sharing
SoFi - The Soft Robotic Fish
Controlling drones and other robots with gestures
Using Muscle Signals to Lift Objects with Robots
Embodied Intelligence Community of Research
Helping robots learn using demonstrations
CSAIL director, robotics, AI, data science, privacy
JShah-headshot

PI
Core/Dual

Julie Shah

Associate Professor
Robotics, machine learning, artificial intelligence
JShah-headshot
T 324-4879

Lead

Interactive Robotics Group
Robots that collaborate with humans to form team plans
Robots that Quickly Adjust Team Plans to Disturbance
Human-Guided Transfer for Robot Learning
Efficient Communication for Human-Machine Teams

Member

Apprenticeship Scheduling: Learning to Schedule From Human Experts
Embodied Intelligence Community of Research
Robotics, machine learning, artificial intelligence
Russ Tedrake

PI
Core/Dual

Russ Tedrake

Professor
Robotics, machine learning, motion planning and control
Russ Tedrake
T 253-1778
RM 32-380B

Lead

Robot Locomotion Group
Localizing External Contact Using Proprioceptive Sensors
Tactile and Physical Perception
Soft Contact Modeling
Self-Driving Vehicles

Member

Tactile and Physical Perception
Task-Driven Co-Optimization Of Articulated Robots
Center for Deployable Machine Learning (CDML)
Embodied Intelligence Community of Research
Robotics, machine learning, motion planning and control
Williams

PI
Core/Dual

Brian Williams

Professor
Robotics, autonomy, artificial intelligence, machine learning
Williams
T 253-2739
RM 32-227

Lead

Model-Based Embedded and Robotics Systems Group
RADMAX
Geordi: A Driver's Assistant for Risk-bounded Maneuvering
Robots Adapting to Human Intent
Risk-aware Network Resource Allocation
Uhura: Personal Assistant that Manages Risk
Artificial Social Intelligence for Successful Teams (ASIST)

Member

Cognitive AI Community of Research
Robotics, autonomy, artificial intelligence, machine learning

22 News Results

System directs camera-equipped drones to maintain framing of an aerial shot.
Algorithms & Theory AI & ML Graphics & Vision Robotics Entertainment Transportation

Cinematography on the fly

In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.

Cinematography on the fly

“It’s really exciting to see our small origami robots doing something with potential important applications to healthcare,” Daniela Rus says. Pictured, an example of a capsule and the unfolded origami device.
Robotics Health Care

Ingestible origami robot can patch wounds inside your stomach!

In experiments involving a simulation of the human esophagus and stomach, researchers at CSAIL, the University of Sheffield, and the Tokyo Institute of Technology have demonstrated a tiny origami robot that can unfold itself from a swallowed capsule and, steered by external magnetic fields, crawl across the stomach wall to remove a swallowed button battery or patch a wound.The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of CSAIL Director Daniela Rus, the Andrew and Erna Viterbi Professor in MIT’s Department of Electrical Engineering and Computer Science.

Ingestible origami robot can patch wounds inside your stomach!