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4 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.

+5

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Algorithms & Theory

Graphics & Vision

Human-Computer Interaction

Programming Languages & Software Engineering

Security & Cryptography

Impact Areas

Lead

Aleksander Madry

Aleksander Madry

Research Group

Distributed Robotics Laboratory

We aim to develop the science of autonomy toward a future with robots and AI systems integrated into everyday life, supporting people with cognitive and physical tasks.

+11

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

Daniela Rus

Research Group

Interactive Robotics Group

The Interactive Robotics Group aims to imagine the future of work by designing collaborative robot teammates that enhance human capability.

Julie Shah

Leads

Julie Shah

Research Areas

Impact Areas

Lead

Julie Shah

Julie Shah

Research Group

Sensing, Learning and Inference

We develop scalable algorithms for analysis of complex, high-dimensional data.

+6

John Fisher

Leads

John Fisher

Research Areas

Graphics & Vision

Impact Areas

Internet of Things

Lead

John Fisher

John Fisher

9 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.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Algorithms & Theory

Human-Computer Interaction

Impact Areas

Leslie Kaelbling

Tomas Lozano-Perez

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

Interactive Robotics Group

Robot scheduling via learning from demonstration for tasks in medicine and more

Leads

Research Areas

Impact Areas

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

Brian Williams

Boris Katz

Zhutian Yang

Research Areas

Computer Architecture

Human-Computer Interaction

Impact Areas

Project

Human-Robot Collaboration in Shared Workspace

Interactive Robotics Group

Models and algorithms to predict in real time the next actions of a human working together with a robot

Leads

Research Areas

Impact Areas

Project

Robots Adapting to Human Intent

Model-Based Embedded and Robotics Systems Group

Cognitive AI Community of Research

Our goal is to develop algorithms for allowing robots to adapt intelligently to humans when they are working together as a team.

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Brian Williams

Project

Sensorized Soft Electric Hands with Handed Shearing Auxetics

Distributed Robotics Laboratory

Putting a twist on traditional pneumatic soft robots via handed shearing auxetics

Lillian Chin

Leads

Lillian Chin

Research Areas

Impact Areas

Lillian Chin

Project

Supervising Robots with Muscle and Brain Signals

Distributed Robotics Laboratory

What if we could control robots more intuitively, using just hand gestures and brainwaves?

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Human-Computer Interaction

Impact Areas

Joseph DelPreto

Project

Wearable Blind Navigation

Distributed Robotics Laboratory

We enable independent navigation for visually impaired users through a wearable feedback system using time-of-light sensors or depth cameras.

Leads

Research Areas

Graphics & Vision

Human-Computer Interaction

Impact Areas

Project

Wearable Ultrasonic Echolocation

The goal of this project is to develop and test a wearable ultrasonic echolocation aid for people who are blind and visually impaired. We combine concepts from engineering, acoustic physics, and neuroscience to make echolocation accessible as a research tool and mobility aid.

Santani Teng

Leads

Santani Teng

Research Areas

Human-Computer Interaction

Impact Areas

Santani Teng

10 People Results

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

RM

Lead

Robust Learning for Autonomous Vehicles

Member

Self-Driving Vehicles

Distributed Robotics Laboratory

PhD Candidate. Deep Learning; Robotics

Brandon Araki

Graduate Student

E araki@csail.mit.edu

Noam Buckman

Graduate Student

PhD Candidate. Autonomous vehicles. Multi-robot systems.

E nbuckman@mit.edu

RM

Member

Distributed Robotics Laboratory

Self-Driving Vehicles

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Lillian Chin

Graduate Student

E ltchin@mit.edu

RM

Lead

Sensorized Soft Electric Hands with Handed Shearing Auxetics

Member

Distributed Robotics Laboratory

Ramin Hasani

Postdoctoral Associate

Machine learning Scientist

E rhasani@csail.mit.edu

Machine learning Scientist

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

RM

Lead

Marine Robotics Group

Self-Driving Vehicles

Member

Embodied Intelligence Community of Research

Robotics, navigation/mapping, self-driving cars

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Safety Standards for Autonomous Vehicles

Member

Self-Driving Vehicles

Distributed Robotics Laboratory

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

PI
Core/Dual

Daniela Rus

Director

CSAIL director, robotics, AI, data science, privacy

E rus@csail.mit.edu

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

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

Wilko Schwarting

Graduate Student

E wilkos@mit.edu

Lead

Safety Standards for Autonomous Vehicles

Planning in Congestion for Autonomous Vehicles

Socially-Compliant Behavior for Autonomous Driving

Member

Self-Driving Vehicles

Distributed Robotics Laboratory

PI
Core/Dual

Julie Shah

Associate Professor

Robotics, machine learning, artificial intelligence

E julie_a_shah@csail.mit.edu

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

8 News Results

Robotic helpers

Robotics Health Care

October 22 '20

What to Expect When You’re Expecting Robots

Book co-authored by Associate Professor Julie Shah and Laura Major SM ’05 explores a future populated with robot helpers.

What to Expect When You’re Expecting Robots

drones/biceps

Robotics Health Care Manufacturing

April 27 '20

Muscle signals can pilot a robot

CSAIL system uses muscle signals to cue a drone’s movement, enabling more natural human-robot communication

Muscle signals can pilot a robot

dutch pm1

AI & ML Robotics Health Care

July 22 '19

Prime Minister Mark Rutte of the Netherlands tours MIT

Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.

Prime Minister Mark Rutte of the Netherlands tours MIT

Barbara Liskov 2

Algorithms & Theory AI & ML Computational Biology Computer Architecture Graphics & Vision Human-Computer Interaction Programming Languages & Software Engineering Robotics Security & Cryptography Systems & Networking Cybersecurity Education Energy Entertainment Health Care Manufacturing Transportation Wireless

June 27 '19

CSAIL hosts first-ever TEDxMIT

Speakers — all women — discuss everything from gravitational waves to robot nurses

CSAIL hosts first-ever TEDxMIT

recycle

AI & ML Robotics Energy Health Care

April 18 '19

Robots that can sort recycling

CSAIL’s "RoCycle" system uses in-hand sensors to detect if an object is paper, metal or plastic.

Robots that can sort recycling

origami gripper

AI & ML Robotics Health Care Manufacturing

March 14 '19

New robot hand is soft and strong

Gripper inspired by “origami magic ball” can grasp wide array of delicate and heavy objects

New robot hand is soft and strong

Drone (image by Melanie Gonick, MIT)

AI & ML Robotics Health Care Transportation

November 01 '18

Fleets of drones could aid searches for lost hikers

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

Fleets of drones could aid searches for lost hikers

“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

May 12 '16

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!