Research

  • Research Areas
  • Impact Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & Machine Learning

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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9 Group Results matching all criteria

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 & Machine Learning
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

55 Project Results matching all criteria

Project

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

Model-Based Embedded and Robotics Systems Group
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

Wilko Schwarting

Rus

Daniela Rus

Research Areas

Algorithms & Theory
Robotics

Impact Areas

Transportation

34 People Results matching all criteria

Joseph DelPreto

Joseph DelPreto

Graduate Student
Works on human-robot interaction using muscle and brain signals.
Joseph DelPreto
RM 32-376A

Lead

Supervising Robots with Muscle and Brain Signals
Brain-Controlled Robots
Distributed Robot Garden
Robot Compiler
SoFi - The Soft Robotic Fish
Soft Robotic Hand
Using Muscle Signals to Lift Objects with Robots

Member

Distributed Robotics Laboratory
Using Muscle Signals to Lift Objects with Robots
Works on human-robot interaction using muscle and brain signals.
Kaelbling

PI
Core/Dual

Leslie Kaelbling

Professor
Robotics, machine learning, motion planning
Kaelbling
T 258-9695
RM 32-333

Lead

Learning and Intelligent Systems
Machine Learning
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, machine learning, motion planning
Lozano-Perez

PI
Core/Dual

Tomas Lozano-Perez

Professor
Robotics, artificial intelligence
Lozano-Perez
T 253-7889
RM 32-333

Lead

Learning and Intelligent Systems
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, artificial intelligence
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
CSAIL director, robotics, AI, data science, privacy

38 News Results matching all criteria

A GelSight sensor attached to a robot’s gripper enables the robot to determine precisely where it has grasped a small screwdriver, removing it from and inserting it back into a slot, even when the gripper screens the screwdriver from the robot’s came
Robotics Manufacturing

Giving robots a sense of touch

Eight years ago, Ted Adelson’s research group at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new sensor technology, called GelSight, that uses physical contact with an object to provide a remarkably detailed 3-D map of its surface. Now, by mounting GelSight sensors on the grippers of robotic arms, two MIT teams have given robots greater sensitivity and dexterity. The researchers presented their work in two papers at the International Conference on Robotics and Automation last week.

Giving robots a sense of touch

System directs camera-equipped drones to maintain framing of an aerial shot.
Algorithms & Theory AI & Machine Learning 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

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory are trying to improve automated planners by giving them the benefit of human intuition.
AI & Machine Learning Robotics

Adding a splash of human intuition to planning algorithms

Every other year, the International Conference on Automated Planning and Scheduling hosts a competition in which computer systems designed by conference participants try to find the best solution to a planning problem, such as scheduling flights or coordinating tasks for teams of autonomous satellites. On all but the most straightforward problems, however, even the best planning algorithms still aren’t as effective as human beings with a particular aptitude for problem-solving — such as MIT students.

Adding a splash of human intuition to planning algorithms

“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!

This 3-D hexapod robot moves via a single motor, which spins a crankshaft that pumps fluid to the robot’s legs. Besides the motor and battery, every component is printed in a single step with no assembly required.
Robotics Manufacturing

First-ever 3-D printed robots made of both solids and liquids

One reason we don’t yet have robot personal assistants buzzing around doing our chores is because making them is hard. Assembling robots by hand is time-consuming, while automation — robots building other robots — is not yet fine-tuned enough to make robots that can do complex tasks.But if humans and robots can’t do the trick, what about 3-D printers?In a new paper, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) present the first-ever technique for 3-D printing robots that involves printing solid and liquid materials at the same time.The new method allows the team to automatically 3-D print dynamic robots in a single step, with no assembly required, using a commercially-available 3-D printer.

First-ever 3-D printed robots made of both solids and liquids

System could help prevent robots from overwhelming human teammates with information.
Robotics

Human-robot teams to the rescue!

Autonomous robots performing a joint task send each other continual updates: “I’ve passed through a door and am turning 90 degrees right.” “After advancing 2 feet I’ve encountered a wall. I’m turning 90 degrees right.” “After advancing 4 feet I’ve encountered a wall.” And so on.Computers, of course, have no trouble filing this information away until they need it. But such a barrage of data would drive a human being crazy.

Human-robot teams to the rescue!

The MIT researchers' centimeter-long origami robot
Robotics

Origami robot self-folds, crawls, climbs, swims, self-destructs

A team of CSAIL researchers have developed a printable origami robot that folds itself up from a flat sheet of plastic when heated and measures about a centimeter from front to back.Weighing only a third of a gram, the robot can swim, climb an incline, traverse rough terrain, and carry a load twice its weight. Other than the self-folding plastic sheet, the robot’s only component is a permanent magnet affixed to its back. Its motions are controlled by external magnetic fields.“The entire walking motion is embedded into the mechanics of the robot body,” says Cynthia R. Sung, a CSAIL graduate student and one of the robot’s co-developers. “In previous [origami] robots, they had to design electronics and motors to actuate the body itself.”

Origami robot self-folds, crawls, climbs, swims, self-destructs

9 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 & Machine Learning
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

55 Project Results

Project

An Interlock for Self Driving Cars

Software Design Group
Computer-Aided Programming
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 & Machine Learning
Human-Computer Interaction
Programming Languages & Software Engineering
Robotics
Systems & Networking

Impact Areas

Transportation
 
Jackson image

Daniel Jackson

Solar-Lezama

Armando Solar-Lezama

Project

Efficient Communication for Human-Machine Teams

Interactive Robotics Group
Our goal is to develop collaborative agents (software or robots) that can efficiently communicate with their human teammates. Key threads involve designing algorithms for inferring human behavior and for decision-making under uncertainty.

X. Jessie Yang

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Leads

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Research Areas

Human-Computer Interaction
Robotics

Impact Areas

Manufacturing

X. Jessie Yang

 

Vaibhav V. Unhelkar

JShah-headshot

Julie Shah

Project

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

Model-Based Embedded and Robotics Systems Group
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

34 People Results

Joseph DelPreto

Joseph DelPreto

Graduate Student
Works on human-robot interaction using muscle and brain signals.
Joseph DelPreto
RM 32-376A

Lead

Supervising Robots with Muscle and Brain Signals
Brain-Controlled Robots
Distributed Robot Garden
Robot Compiler
SoFi - The Soft Robotic Fish
Soft Robotic Hand
Using Muscle Signals to Lift Objects with Robots

Member

Distributed Robotics Laboratory
Using Muscle Signals to Lift Objects with Robots
Works on human-robot interaction using muscle and brain signals.
Kaelbling

PI
Core/Dual

Leslie Kaelbling

Professor
Robotics, machine learning, motion planning
Kaelbling
T 258-9695
RM 32-333

Lead

Learning and Intelligent Systems
Machine Learning
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, machine learning, motion planning
Lozano-Perez

PI
Core/Dual

Tomas Lozano-Perez

Professor
Robotics, artificial intelligence
Lozano-Perez
T 253-7889
RM 32-333

Lead

Learning and Intelligent Systems
Learning and Optimization
Robotic Planning and Manipulation with Force Information
Reliable Robotic Construction of Multi-object Arrangements
Learning and Planning with Compressed Linear Expectation Models
Active Learning of Models for Planning
Task and Motion Planning for Autonomous Robots
Robust Intelligent Robots

Member

Robust Intelligent Robots
Robotics, artificial intelligence
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
CSAIL director, robotics, AI, data science, privacy

38 News Results