Research

Impact Areas

Lead

Daniela Rus

+13

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

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

Research Group

Marine Robotics Group

Our projects are centered around the problems of navigation and mapping for autonomous mobile robots operating in underwater and terrestrial environments.

John Leonard

Leads

John Leonard

Research Areas

Impact Areas

Lead

John Leonard

Research Group

Model-Based Embedded and Robotics Systems Group

We are working to elevate robots from mechanical creations controlled by low-level scripts with a considerable amount of human guidance to truly cognitive robots.

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Lead

Brian Williams

Research Group

Robot Locomotion Group

We research building machines which exploit their natural dynamics to achieve extraordinary agility and efficiency.

Russ Tedrake

Leads

Russ Tedrake

Research Areas

Impact Areas

Lead

Russ Tedrake

Research Group

Robust Robotics Group

We build unmanned vehicles that can fly and drive without maps or GPS.

Nicholas Roy

Leads

Nicholas Roy

Research Areas

Impact Areas

Lead

Nicholas Roy

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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Community of Research

Cognitive AI Community of Research

This CoR aims to develop AI technology that synthesizes symbolic reasoning, probabilistic reasoning for dealing with uncertainty in the world, and statistical methods for extracting and exploiting regularities in the world, into an integrated picture of intelligence that is informed by computational insights and by cognitive science.

Howard Shrobe

Leads

Howard Shrobe

Research Areas

Impact Areas

Lead

Howard Shrobe

Community of Research

Embodied Intelligence Community of Research

Our goal is to understand the nature of intelligent behavior in the physical world, through the study of human intelligence and the design and implementation of intelligent robots.

+12

Leslie Kaelbling

Leads

Leslie Kaelbling

Research Areas

Lead

Leslie Kaelbling

+12

Leslie Kaelbling

Research Group

Computational Design and Fabrication Group

Our research centers on digital manufacturing, 3D printing and computer graphics, as well as computational photography and displays, and virtual humans and robotics.

Wojciech Matusik

Leads

Wojciech Matusik

Research Areas

Impact Areas

Lead

Wojciech Matusik

Research Group

Sensing, Learning and Inference

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

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Internet of Things

Lead

John Fisher

58 Project Results matching all criteria

Project

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

We are studying how best to implement bilateral control and make it acceptable to drivers

Berthold Horn

Leads

Berthold Horn

Research Areas

Impact Areas

Berthold Horn

Project

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

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.

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Brian Williams

Project

Printable Hydraulics

Printable Hydraulics allows fluid-actuated robots to be automatically fabricated using 3D printers.

Youbin Kim

Leads

Research Areas

Impact Areas

Youbin Kim

Project

Inferring 3D Shape Distributions for Robot Perception and Planning

Robust Robotics Group

Our goal is to develop methods for a mobile robot to reason about volumetric spatial uncertainty of objects in its environment.

Nicholas Roy

Leads

Nicholas Roy

Research Areas

Impact Areas

Nicholas Roy

Project

Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

Leads

Research Areas

Project

3D-Printed Organic Solar Cells

Using our inkjet printer system, the goal of this project is to produce a light sensor / solar cell that is composed entirely of 3D printed materials.

James Minor

Leads

James Minor

Research Areas

Impact Areas

James Minor

Project

Computational Multicopter Design

A system that can help you to design our own drone!

Tao Du

Leads

Tao Du

Research Areas

Impact Areas

Tao Du

Project

Localizing External Contact Using Proprioceptive Sensors

Robust Robotics Group

The goal is to detect when a robot is contacting the world using only proprioceptive sensors, i.e. joint position and torque sensors, imu's etc.

Russ Tedrake

Leads

Russ Tedrake

Research Areas

Russ Tedrake

Project

Robotic Planning and Manipulation with Force Information

Our goal is to allow planners to exploit the forces and contacts between objects so as to carry out complex manipulation tasks in the presence of uncertainty.

Leads

Gustavo N. Goretkin

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Project

Robots Adapting to Human Intent

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

Tactile and Physical Perception

Robot Locomotion Group

We aim to provide our robots with accurate and robust object pose estimates by taking advantage of tactile, geometric, and physical information.

Gregory Izatt

Russ Tedrake

Leads

Gregory Izatt

Russ Tedrake

Research Areas

Gregory Izatt

Russ Tedrake

Project

Reliable Robotic Construction of Multi-object Arrangements

Our goal is to improve the reliability of robot manipulation by creating models that predict the range of results from interactions between multiple objects and then using these models to plan for reliable manipulation.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Leslie Kaelbling

Tomas Lozano-Perez

Project

Learning and Planning with Compressed Linear Expectation Models

Our goal is to learn models suitable for efficient planning from data obtained from interactions with a dynamical system.

Leads

Clement Gehring

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Project

Active Learning of Models for Planning

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

Impact Areas

Leslie Kaelbling

Tomas Lozano-Perez

Project

Task and Motion Planning for Autonomous Robots

Our goal is to design algorithms that enable robots to operate in human environments by simultaneously reasoning about high-level task actions as well as low-level motions.

Leads

Caelan Garrett

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Project

Deterministic Algorithms for Robotic Task and Motion Planning

Our goal is to investigate deterministic algorithms for robotic task and motion planning.

Leads

Research Areas

Impact Areas

Project

Task-Driven Co-Optimization Of Articulated Robots

A robot's physical form and its motion are innately coupled - in order to change its physical design, one must often change the way it moves, and vice versa. Can computers automatically and simultaneously design robot structure and motion?

Andrew Spielberg

Leads

Andrew Spielberg

Research Areas

Andrew Spielberg

Project

Safety Standards for Autonomous Vehicles

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 Liebenwein

Wilko Schwarting

Daniela Rus

Research Areas

Impact Areas

Project

SoFi - The Soft Robotic Fish

Exploration of underwater life with an acoustically controlled soft robotic fish

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Joseph DelPreto

Project

Soft Contact Modeling

Robot Locomotion Group

We are juggling a puck sliding on a table, using only a pretensioned rubber band.

Daniela Rus

Russ Tedrake

Leads

Daniela Rus

Russ Tedrake

Research Areas

Daniela Rus

Russ Tedrake

Project

Wearable Blind Navigation

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

Leads

Research Areas

Impact Areas

Project

Supervising Robots with Muscle and Brain Signals

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

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Joseph DelPreto

Project

Human-Robot Collaboration in Shared Workspace

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 that collaborate with humans to form team plans

People, including doctors, nurses, and military personnel, often learn their roles in complex organizations through a training and apprenticeship process.

Julie Shah

Leads

Julie Shah

Research Areas

Impact Areas

Robust Learning for Autonomous Vehicles

Julie Shah

36 People Results matching all criteria

Murad Abu-Khalaf

Program Manager, Toyota-CSAIL Joint Research Center

E murad@mit.edu

T 452-2859

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Member

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

Member

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Richard Camilli

Research Affiliate

E romeo@mit.edu

Member

Sensorized Soft Electric Hands with Handed Shearing Auxetics

Lillian Chin

Graduate Student

E ltchin@mit.edu

Lead

Member

Supervising Robots with Muscle and Brain Signals

Joseph DelPreto

Graduate Student

Works on human-robot interaction using muscle and brain signals.

E delpreto@csail.mit.edu

RM 32-376A

Lead

Brain-Controlled Robots

Distributed Robot Garden

Robot Compiler

SoFi - The Soft Robotic Fish

Soft Robotic Hand

Using Muscle Signals to Lift Objects with Robots

Controlling drones and other robots with gestures

Helping robots learn using demonstrations

Member

Works on human-robot interaction using muscle and brain signals.

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

Kevin Doherty

Graduate Student

E KDoherty@mit.edu

RM 32-235B

Tao Du

Graduate Student

CS PhD student

E taodu@csail.mit.edu

Lead

Learning and Planning with Compressed Linear Expectation Models

CS PhD student

Michael Foshey

Mechanical Engineer/Project Manager

Additive Manufacturing, Robotics, Digital Fabrication

E mfoshey@mit.edu

Additive Manufacturing, Robotics, Digital Fabrication

Clement Gehring

Graduate Student

E gehring@csail.mit.edu

Lead

Member

Igor Gilitschenski

Research Scientist

E igilitschenski@mit.edu

Member

Ramin Hasani

Postdoctoral Associate

Machine learning Scientist

E rhasani@csail.mit.edu

Machine learning Scientist

Rachel Holladay

Graduate Student

Robotics, motion planning, manipulation

E Rhollada@mit.edu

Member

Robotics, motion planning, manipulation

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Vision, computational imaging

PI
Core/Dual

Leslie Kaelbling

Professor

Robotics, machine learning, motion planning

E lpk@csail.mit.edu

T 258-9695

Lead

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Member

Robotics, machine learning, motion planning

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

Lead

Marine Robotics Group

Member

Safety Standards for Autonomous Vehicles

Robotics, navigation/mapping, self-driving cars

Shen Li

Graduate Student

E SHENLI@mit.edu

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Member

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

Sandra Liu

Graduate Student

Researching soft robotic finger proprioception and tactile sensing

E sqliu@mit.edu

Researching soft robotic finger proprioception and tactile sensing

PI
Core/Dual

Tomas Lozano-Perez

Professor

Robotics, artificial intelligence

E tlp@csail.mit.edu

T 253-7889

Lead

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Member

Robotics, artificial intelligence

PI
Core/Dual

Wojciech Matusik

Professor

Graphics, vision, fabrication, robotics, hci

E wojciech@csail.mit.edu

T 324-8432

Lead

Member

Simit: A Language for Computing on Sparse Systems

InstantCAD: Interactive Design Exploration/Optimization for CAD Models

Graphics, vision, fabrication, robotics, hci

Martin Nisser

Graduate Student

E nisser@mit.edu

Member

51 News Results matching all criteria

Algorithms & Theory Robotics

November 30 '20

Computer-aided creativity in robot design

MIT researchers’ new system optimizes the shape of robots for traversing various terrain types.

Computer-aided creativity in robot design

A neural network learns when it should not be trusted

AI & ML Robotics Transportation

November 24 '20

A neural network learns when it should not be trusted

A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes.

A neural network learns when it should not be trusted

Algorithms & Theory Robotics Transportation

October 26 '20

Autonomous boats could be your next ride

Five years in the making, MIT’s autonomous floating vessels get a size upgrade and learn a new way to communicate aboard the waters.

Autonomous boats could be your next ride

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

Robotics

August 07 '20

3 Questions: John Leonard on the future of autonomous vehicles

MIT Task Force on the Work of the Future examines job changes in the AV transition and how training can help workers move into careers that support mobility systems.

3 Questions: John Leonard on the future of autonomous vehicles

Robotics Manufacturing

July 13 '20

Letting robots manipulate cables

Robotic gripper with soft sensitive fingers developed at MIT CSAIL can handle cables with unprecedented dexterity.

Letting robots manipulate cables

AI & ML Robotics

June 30 '20

Lozano-Perez wins 2021 IEEE Robotics and Automation Award

This week it was announced that MIT professor and CSAIL principal investigator Tomas Lozano-Perez has been awarded the 2021 IEEE Robotics and Automation Award for his “foundational contributions to robot motion planning and visionary leadership in the field.”

Lozano-Perez wins 2021 IEEE Robotics and Automation Award

Algorithms & Theory Robotics

June 02 '20

New glove lets you incorporate real-life objects into virtual worlds

Algorithms & Theory Robotics Manufacturing

June 01 '20

Giving soft robots senses

In a pair of papers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), two teams enable better sense and perception for soft robotic grippers.

Giving soft robots senses

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

AI & ML Robotics

April 01 '20

Teaching robots workplace etiquette

MIT framework goes past the "how" of communication to teach robots the "if" and "when"

Teaching robots workplace etiquette

Algorithms & Theory AI & ML Robotics

March 05 '20

Showing robots how to do your chores

By observing humans, robots learn to perform complex tasks, such as setting a table.

Showing robots how to do your chores

AI & ML Robotics

February 24 '20

To self-drive in the snow, look under the road

Weather’s a problem for autonomous cars. But MIT’s new system shows promise by using “ground-penetrating radar” instead of cameras or lasers

To self-drive in the snow, look under the road

Algorithms & Theory AI & ML Robotics

February 12 '20

“Sensorized” skin helps soft robots find their bearings

Flexible sensors and an artificial intelligence model tell deformable robots how their bodies are positioned in a 3D environment.

“Sensorized” skin helps soft robots find their bearings

AI & ML Robotics Cybersecurity Internet of Things Transportation

January 13 '20

WaPo op-ed: how to regulate AI properly

In a Washington Post op-ed, CSAIL's R. David Edelman outlines how to regulate AI properly

WaPo op-ed: how to regulate AI properly

Algorithms & Theory AI & ML Robotics

November 13 '19

Autonomous system improves environmental sampling at sea

Robotic boats could more rapidly locate the most valuable sampling spots in uncharted waters.

Autonomous system improves environmental sampling at sea

AI & ML Robotics Manufacturing Transportation

October 30 '19

Self-transforming robot blocks jump, spin, flip, and identify each other

Developed at MIT’s Computer Science and Artificial Intelligence Laboratory, a team of robots can self-assemble to form different structures with applications in inspection, disaster response, and manufacturing

Self-transforming robot blocks jump, spin, flip, and identify each other

Algorithms & Theory AI & ML Robotics Transportation

August 29 '19

MIT’s fleet of autonomous boats can now shapeshift

New capabilities allow “roboats” to change configurations to form pop-up bridges, stages, and other structures.

MIT’s fleet of autonomous boats can now shapeshift

Algorithms & Theory Graphics & Vision Robotics Manufacturing

July 22 '19

Automated system generates robotic parts for novel tasks

When designing actuators involves too many variables for humans to test by hand, this system can step in.

Automated system generates robotic parts for novel tasks

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

Algorithms & Theory Robotics Transportation

June 27 '19

Autonomous boats can target and latch onto each other

Fleet of “roboats” could collect garbage or self-assemble into floating structures in Amsterdam’s many canals.

Autonomous boats can target and latch onto each other

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

AI & ML Robotics Manufacturing

May 22 '19

This robot helps you lift objects — by looking at your biceps

CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.

This robot helps you lift objects — by looking at your biceps

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

12 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.

Aleksander Madry

Leads

Aleksander Madry

Research Areas

Impact Areas

Lead

Aleksander Madry

Community of Research

Cognitive AI Community of Research

Howard Shrobe

Leads

Howard Shrobe

Research Areas

Impact Areas

Lead

Howard Shrobe

Research Group

Computational Design and Fabrication Group

Our research centers on digital manufacturing, 3D printing and computer graphics, as well as computational photography and displays, and virtual humans and robotics.

Wojciech Matusik

Leads

Wojciech Matusik

Research Areas

Impact Areas

Lead

Wojciech Matusik

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.

+13

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

+13

Daniela Rus

Community of Research

Embodied Intelligence Community of Research

Our goal is to understand the nature of intelligent behavior in the physical world, through the study of human intelligence and the design and implementation of intelligent robots.

+12

Leslie Kaelbling

Leads

Leslie Kaelbling

Research Areas

Lead

Leslie Kaelbling

+12

Leslie Kaelbling

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

Research Group

Learning and Intelligent Systems

We conduct interdisciplinary research aimed at discovering the principles underlying the design of artificially intelligent robots.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Lead

Leslie Kaelbling

Tomas Lozano-Perez

Leslie Kaelbling

Tomas Lozano-Perez

Research Group

Marine Robotics Group

Our projects are centered around the problems of navigation and mapping for autonomous mobile robots operating in underwater and terrestrial environments.

John Leonard

Leads

John Leonard

Research Areas

Impact Areas

Lead

John Leonard

Research Group

Model-Based Embedded and Robotics Systems Group

We are working to elevate robots from mechanical creations controlled by low-level scripts with a considerable amount of human guidance to truly cognitive robots.

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Lead

Brian Williams

Research Group

Robot Locomotion Group

We research building machines which exploit their natural dynamics to achieve extraordinary agility and efficiency.

Russ Tedrake

Leads

Russ Tedrake

Research Areas

Impact Areas

Lead

Russ Tedrake

Research Group

Robust Robotics Group

We build unmanned vehicles that can fly and drive without maps or GPS.

Nicholas Roy

Leads

Nicholas Roy

Research Areas

Impact Areas

Lead

Nicholas Roy

Research Group

Sensing, Learning and Inference

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

John Fisher

Leads

John Fisher

Research Areas

Impact Areas

Internet of Things

Lead

John Fisher

58 Project Results

Project

3D-Printed Organic Solar Cells

Using our inkjet printer system, the goal of this project is to produce a light sensor / solar cell that is composed entirely of 3D printed materials.

James Minor

Leads

James Minor

Research Areas

Impact Areas

James Minor

Project

Active Learning of Models for Planning

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

Impact Areas

Computer-Aided Programming

Leslie Kaelbling

Tomas Lozano-Perez

Project

An Interlock for Self Driving Cars

Software Design Group

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.

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Impact Areas

Daniel Jackson

Armando Solar-Lezama

Project

Apprenticeship Scheduling: Learning to Schedule From Human Experts

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

Leads

Research Areas

Impact Areas

Computation Structures Group

Project

Artificial Social Intelligence for Successful Teams (ASIST)

Computational Cognitive Science 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

Impact Areas

Wireless

Project

Blood Pressure Imager

Development of affordable wearable continuous blood pressure monitor based on radial arterial pulse imaged from the skin surface of human wrist

Mumin Jin

Aya G. Halawi

Jianhua Li

Leads

Abhijit Biswas

Ted Adelson

Mandayam A. Srinivasan

Mohan Thanikachalam

Research Areas

Computational Biology

Mumin Jin

Aya G. Halawi

Jianhua Li

Project

Brain-Controlled Robots

This project aims to let people correct robot mistakes with nothing more than their brain signals - to allow robots to adapt to humans rather than the other way around

Frank H. Guenther

Daniela Rus

Joseph DelPreto

Leads

Daniela Rus

Joseph DelPreto

Research Areas

Frank H. Guenther

Daniela Rus

Joseph DelPreto

Project

Computational Multicopter Design

A system that can help you to design our own drone!

Tao Du

Leads

Tao Du

Research Areas

Impact Areas

Tao Du

Project

Controlling drones and other robots with gestures

Interact with robots and other devices by gesturing, using wearable muscle and motion sensors

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Joseph DelPreto

Project

Deep Inverse Planning

Deep inverse planning for learning from high-dimensional demonstrations

Leads

Research Areas

Project

Deterministic Algorithms for Robotic Task and Motion Planning

Our goal is to investigate deterministic algorithms for robotic task and motion planning.

Leads

Research Areas

Impact Areas

Project

Distributed Robot Garden

The robot garden provides an aesthetically pleasing educational platform that can visualize computer science concepts and encourage young students to pursue programming and robotics.

Leads

Joseph DelPreto

Lindsay Sanneman

Daniela Rus

Research Areas

Impact Areas

Project

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

We are studying how best to implement bilateral control and make it acceptable to drivers

Berthold Horn

Leads

Berthold Horn

Research Areas

Impact Areas

Berthold Horn

Project

Drones that Drive

Multi-robot path planning for robot swarms that can both fly and drive

Leads

Research Areas

Impact Areas

Project

Efficient Communication for Human-Machine Teams

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

Julie Shah

Leads

Julie Shah

Research Areas

Impact Areas

X. Jessie Yang

Julie Shah

Project

Enabling Semantic Understanding for Autonomous Marine Robots

Our goal is to develop unsupervised or minimally supervised marine learning frameworks that allow autonomous underwater vehicles (AUVs) to explore unknown marine environments and communicate their findings in a semantically meaningful manner.

Yogesh Girdhar

Genevieve Flaspohler

Leads

Genevieve Flaspohler

Research Areas

Yogesh Girdhar

Genevieve Flaspohler

Project

Fast Lightweight Autonomy

The goal of the FLA program is to explore non-traditional perception and autonomy methods that could enable a new class of algorithms for minimalistic high-speed navigation in cluttered environments.

Nick Villanueva

Jake Ware

Leads

Jake Ware

Research Areas

Nick Villanueva

Jake Ware

Project

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

Cognitive AI Community of Research

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Brian Williams

Project

Helping robots learn using demonstrations

Help robots learn faster by providing demonstrations when they need help

Joseph DelPreto

Leads

Joseph DelPreto

Research Areas

Impact Areas

Joseph DelPreto

Project

Human-Guided Transfer for Robot Learning

Our goal is to develop computational learning models for agents that intelligently use various types of human feedback to help an agent adapt and transfer knowledge to new situations.

Julie Shah

Leads

Julie Shah

Research Areas

Impact Areas

Julie Shah

Project

Human-Robot Collaboration in Shared Workspace

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

Inferring 3D Shape Distributions for Robot Perception and Planning

Robust Robotics Group

Our goal is to develop methods for a mobile robot to reason about volumetric spatial uncertainty of objects in its environment.

Nicholas Roy

Leads

Nicholas Roy

Research Areas

Impact Areas

Nicholas Roy

Project

Learning and Planning with Compressed Linear Expectation Models

Our goal is to learn models suitable for efficient planning from data obtained from interactions with a dynamical system.

Leads

Clement Gehring

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Project

Learning complex task structures from demonstrations

Our goal is to learn the structure of complex multi-step tasks from demonstration of human experts.

Ankit Shah

Leads

Ankit Shah

Research Areas

Impact Areas

Robust Learning for Autonomous Vehicles

Ankit Shah

36 People Results

Murad Abu-Khalaf

Program Manager, Toyota-CSAIL Joint Research Center

E murad@mit.edu

T 452-2859

Alexander Amini

Graduate Student

PhD Candidate. Deep Learning; Robotics

E Amini@mit.edu

Lead

Member

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

Member

PhD Candidate. Autonomous vehicles. Multi-robot systems.

Richard Camilli

Research Affiliate

E romeo@mit.edu

Member

Sensorized Soft Electric Hands with Handed Shearing Auxetics

Lillian Chin

Graduate Student

E ltchin@mit.edu

Lead

Member

Supervising Robots with Muscle and Brain Signals

Joseph DelPreto

Graduate Student

Works on human-robot interaction using muscle and brain signals.

E delpreto@csail.mit.edu

RM 32-376A

Lead

Brain-Controlled Robots

Distributed Robot Garden

Robot Compiler

SoFi - The Soft Robotic Fish

Soft Robotic Hand

Using Muscle Signals to Lift Objects with Robots

Controlling drones and other robots with gestures

Helping robots learn using demonstrations

Member

Works on human-robot interaction using muscle and brain signals.

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

Member

Kevin Doherty

Graduate Student

E KDoherty@mit.edu

RM 32-235B

Tao Du

Graduate Student

CS PhD student

E taodu@csail.mit.edu

Lead

Learning and Planning with Compressed Linear Expectation Models

CS PhD student

Michael Foshey

Mechanical Engineer/Project Manager

Additive Manufacturing, Robotics, Digital Fabrication

E mfoshey@mit.edu

Additive Manufacturing, Robotics, Digital Fabrication

Clement Gehring

Graduate Student

E gehring@csail.mit.edu

Lead

Member

Igor Gilitschenski

Research Scientist

E igilitschenski@mit.edu

Member

Ramin Hasani

Postdoctoral Associate

Machine learning Scientist

E rhasani@csail.mit.edu

Machine learning Scientist

Rachel Holladay

Graduate Student

Robotics, motion planning, manipulation

E Rhollada@mit.edu

Member

Robotics, motion planning, manipulation

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Vision, computational imaging

PI
Core/Dual

Leslie Kaelbling

Professor

Robotics, machine learning, motion planning

E lpk@csail.mit.edu

T 258-9695

Lead

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Member

Robotics, machine learning, motion planning

PI
Core/Dual

John Leonard

Professor

Robotics, navigation/mapping, self-driving cars

E jleonard@csail.mit.edu

T 253-5305

Lead

Marine Robotics Group

Member

Safety Standards for Autonomous Vehicles

Robotics, navigation/mapping, self-driving cars

Shen Li

Graduate Student

E SHENLI@mit.edu

Lucas Liebenwein

Graduate Student

Student and Researcher | Robotics, AI | Advanced Autonomy

E lucasl@mit.edu

RM 32-376C

Lead

Member

Coresets for Machine Learning Algorithms

Student and Researcher | Robotics, AI | Advanced Autonomy

Sandra Liu

Graduate Student

Researching soft robotic finger proprioception and tactile sensing

E sqliu@mit.edu

Researching soft robotic finger proprioception and tactile sensing

PI
Core/Dual

Tomas Lozano-Perez

Professor

Robotics, artificial intelligence

E tlp@csail.mit.edu

T 253-7889

Lead

Robotic Planning and Manipulation with Force Information

Reliable Robotic Construction of Multi-object Arrangements

Learning and Planning with Compressed Linear Expectation Models

Member

Robotics, artificial intelligence

PI
Core/Dual

Wojciech Matusik

Professor

Graphics, vision, fabrication, robotics, hci

E wojciech@csail.mit.edu

T 324-8432

Lead

Member

Simit: A Language for Computing on Sparse Systems

InstantCAD: Interactive Design Exploration/Optimization for CAD Models

Graphics, vision, fabrication, robotics, hci

Martin Nisser

Graduate Student

E nisser@mit.edu

Member