Research

Impact Areas
Research Areas

Filter options

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.

+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.

+13

Daniela Rus

Leads

Daniela Rus

Research Areas

Impact Areas

Lead

Daniela Rus

Daniela Rus

Research Group

Geometric Data Processing Group

Our group studies geometric problems in computer graphics, computer vision, machine learning, optimization, and other disciplines.

+6

Justin Solomon

Leads

Justin Solomon

Research Areas

Algorithms & Theory

Graphics & Vision

Impact Areas

Lead

Justin Solomon

Justin Solomon

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

Learning and Intelligent Systems

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

+5

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

John Leonard

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

Russ Tedrake

Research Group

Robust Robotics Group

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

+2

Nicholas Roy

Leads

Nicholas Roy

Research Areas

Impact Areas

Lead

Nicholas Roy

Nicholas Roy

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

Community of Research

Vertical AI Community of Research

This CoR takes a unified approach to cover the full range of research areas required for success in artificial intelligence, including hardware, foundations, software systems, and applications.

+7

Martin Rinard

Leads

Martin Rinard

Research Areas

Algorithms & Theory

Graphics & Vision

Programming Languages & Software Engineering

Security & Cryptography

Systems & Networking

Impact Areas

Internet of Things

Lead

Martin Rinard

Martin Rinard

Research Group

Vision Group

Our researchers create state-of-the-art systems to better recognize objects, people, scenes, behaviors and more, with applications in health-care, gaming, tagging systems and more.

Leads

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Research Areas

Graphics & Vision

Impact Areas

Lead

Ted Adelson

Fredo Durand

John Fisher

William Freeman

Polina Golland

Eric Grimson

Berthold Horn

Aude Oliva

Antonio Torralba

Community of Research

Visual Computing Community of Research

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

+10

William Freeman

Leads

William Freeman

Research Areas

Graphics & Vision

Impact Areas

Lead

William Freeman

William Freeman

29 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

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.

Daniel Jackson

Armando Solar-Lezama

Leads

Daniel Jackson

Armando Solar-Lezama

Research Areas

Human-Computer Interaction

Programming Languages & Software Engineering

Systems & Networking

Impact Areas

Daniel Jackson

Armando Solar-Lezama

Project

Artificial Co-driver

Computer Graphics Group

By observing driver in real road situations, we learn a computer assistant that can make driving easier, safer and more enjoyable.

Leads

Research Areas

Graphics & Vision

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

Characterizing the Sensitivity of Vision-in-the-Loop Driving Controllers Using CG

Computer Graphics Group

We work on understanding and improving vision algorithms used for self driving cars, by introducing synthetic data-sets to the loop.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Computational Multicopter Design

Computer Graphics Group

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

Tao Du

Leads

Tao Du

Research Areas

Graphics & Vision

Impact Areas

Tao Du

Project

Crowdsourcing in Graphics and Vision

Computational Design and Fabrication Group

Our goal is to develop new applications and algorithms that leverage the skills of distributed crowdworkers, notably for image and video processing applications.

Alexandre Kaspar

Leads

Alexandre Kaspar

Research Areas

Graphics & Vision

Human-Computer Interaction

Impact Areas

Alexandre Kaspar

Project

Deterministic Algorithms for Robotic Task and Motion Planning

Learning and Intelligent Systems

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

Leads

Research Areas

Algorithms & Theory

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

Graphics & Vision

Systems & Networking

Impact Areas

Berthold Horn

Project

Drones that Drive

Distributed Robotics Laboratory

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

Leads

Research Areas

Algorithms & Theory

Impact Areas

Project

Geometry and topology for scientific computing and shape analysis

Geometric Data Processing Group

Computer Graphics Group

Developing state-of-the-art tools that process 3D surfaces and volumes

+1

Justin Solomon

Leads

Justin Solomon

Research Areas

Algorithms & Theory

Graphics & Vision

Impact Areas

Justin Solomon

Project

Geometry in Large-Scale Machine Learning

Machine Learning

Data often has geometric structure which can enable better inference; this project aims to scale up geometry-aware techniques for use in machine learning settings with lots of data, so that this structure may be utilized in practice.

Justin Solomon

Stefanie Jegelka

Leads

Justin Solomon

Stefanie Jegelka

Research Areas

Graphics & Vision

Impact Areas

Internet of Things

Justin Solomon

Stefanie Jegelka

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.

Brian Williams

Leads

Brian Williams

Research Areas

Impact Areas

Brian Williams

Project

Peripheral Vision and Driver Behavior

Our goal is to understand what drivers can perceive when they are not attending to the road ahead and the consequences of their perceptions for safety.

Leads

Research Areas

Graphics & Vision

Impact Areas

Project

Planning in Congestion for Autonomous Vehicles

Distributed Robotics Laboratory

Our goal is to develop an autonomous vehicle control strategy that adapts on-line to varying levels of congestion in the environment.

Leads

Alyssa Pierson

Wilko Schwarting

Sertac Karaman

Daniela Rus

Research Areas

Impact Areas

Project

Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting

Geometric Data Processing Group

Gerrymandering is a direct threat to our democracy, undermining founding principles like equal protection under the law and eroding public confidence in elections.

Justin Solomon

Leads

Justin Solomon

Research Areas

Algorithms & Theory

Graphics & Vision

Human-Computer Interaction

Impact Areas

Justin Solomon

Project

Printable Hydraulics

Distributed Robotics Laboratory

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

Youbin Kim

Leads

Research Areas

Impact Areas

Youbin Kim

Project

Robot Compiler

Distributed Robotics Laboratory

The Robot Compiler allows non-engineering users to rapidly fabricate customized robots, facilitating the proliferation of robots in everyday life. It thereby marks an important step towards the realization of personal robots that have captured imaginations for decades.

Leads

Joseph DelPreto

Andrew Spielberg

Daniela Rus

Research Areas

Algorithms & Theory

Human-Computer Interaction

Impact Areas

Internet of Things

Project

Robotic Planning and Manipulation with Force Information

Learning and Intelligent Systems

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

Robust Intelligent Robots

Learning and Intelligent Systems

How can a mobile-manipulation robot operate robustly and flexibly in complicated uncertain environments, such as households and disaster zones.

Leslie Kaelbling

Tomas Lozano-Perez

Leads

Leslie Kaelbling

Tomas Lozano-Perez

Research Areas

Impact Areas

Leslie Kaelbling

Tomas Lozano-Perez

Project

Robust Learning for Autonomous Vehicles

Distributed Robotics Laboratory

Our goal is to create a theoretical framework and effective machine learning algorithms for robust, reliable control of autonomous vehicles. Key threads include developing metrics of confidence; and designing deep learning algorithms for parallel autonomy.

Daniela Rus

Alexander Amini

Leads

Daniela Rus

Alexander Amini

Research Areas

Impact Areas

Daniela Rus

Alexander Amini

Project

Route and Motion Planning for Mobility-on-Demand with Rules of the Road

Distributed Robotics Laboratory

Ensuring correct behavior of autonomous vehicles with respect to rules of the road

Sertac Karaman

Daniela Rus

Leads

Sertac Karaman

Daniela Rus

Research Areas

Impact Areas

Sertac Karaman

Daniela Rus

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 Liebenwein

Wilko Schwarting

Daniela Rus

Research Areas

Algorithms & Theory

Impact Areas

Project

Self-Driving Vehicles

Distributed Robotics Laboratory

We want to help to make driving more comfortable, environmentally friendly and above all safer!

+10

Leads

Russ Tedrake

John Leonard

Daniela Rus

Sertac Karaman

Research Areas

Programming Languages & Software Engineering

Impact Areas

27 People Results

PI
Core/Dual

Ted Adelson

Professor

Vision, touch sensing, robotics

E adelson@csail.mit.edu

T 253-0645

RM

Lead

Blood Pressure Imager

Member

Embodied Intelligence Community of Research

Visual Computing Community of Research

Vision, touch sensing, robotics

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

PI
Core/Dual

Regina Barzilay

Professor

Natural language processing, machine learning

E regina@csail.mit.edu

T 258-5706

RM

Lead

Natural Language Processing Group

Aspect-Augmented Adversarial Networks for Domain Adaptation

Member

Vertical AI Community of Research

Applied Machine Learning Community of Research

Natural language processing, machine learning

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

Yu-An Chung

Graduate Student

Speech and language processing, machine learning

E andyyuan@mit.edu

RM

Lead

Unsupervised Speech Processing

Member

Spoken Language Systems Group

Speech and language processing, machine learning

Mustafa Doga Dogan

Graduate Student

E doga@mit.edu

RM

Member

HCI Engineering Group

PI
Core/Dual

Fredo Durand

Professor

Vision, graphics, computational photography

E fredo@csail.mit.edu

T 253-7223

RM

Lead

Computer Graphics Group

Human-Computer Interaction

Computational Understanding of Visualizations and Graphic Designs

Member

Computational Bounce Flash for Indoor Portraits

Visual Computing Community of Research

Vision, graphics, computational photography

PI
Core/Dual

John Fisher

Senior Research Scientist

Vision, machine learning, signal processing

E fisher@csail.mit.edu

T 253-0788

RM

Lead

Sensing, Learning and Inference

Member

Visual Computing Community of Research

Vision, machine learning, signal processing

PI
Core/Dual

William Freeman

Professor

Vision, machine learning, Bayesian modeling

E billf@csail.mit.edu

T 253-8828

RM

Lead

Computer Graphics Group

Visual Computing Community of Research

Member

Single-Image 3D Interpreter Network

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Learning the Arrow of Time from Videos

3D Generative Adversarial Networks

Embodied Intelligence Community of Research

Vision, machine learning, Bayesian modeling

Igor Gilitschenski

Research Scientist

E igilitschenski@mit.edu

Member

Distributed Robotics Laboratory

Self-Driving Vehicles

Evan Hernandez

Graduate Student

Natural language processing, machine learning, interpretability

E dez@mit.edu

Natural language processing, machine learning, interpretability

PI
Core/Dual

Berthold Horn

Professor

Vision, computational imaging

E bkph@csail.mit.edu

T 253-5863

RM

Lead

Driver-Friendly Bilateral Control for Suppressing Traffic Instabilities

Member

Visual Computing Community of Research

Vision, computational imaging

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

Alyssa Pierson

Research Scientist

E apierson@csail.mit.edu

RM

Lead

Planning in Congestion for Autonomous Vehicles

Socially-Compliant Behavior for Autonomous Driving

Member

Distributed Robotics Laboratory

Self-Driving Vehicles

PI
Core/Dual

Nicholas Roy

Professor

Robotics, autonomy, artificial intelligence, motion planning

E nickroy@csail.mit.edu

T 253-2517

RM

Lead

Robust Robotics Group

Inferring 3D Shape Distributions for Robot Perception and Planning

Member

Enabling Semantic Understanding for Autonomous Marine Robots

Embodied Intelligence Community of Research

Robotics, autonomy, artificial intelligence, motion planning

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

Dmitriy Smirnov

Graduate Student

https://people.csail.mit.edu/smirnov/

E smirnov@mit.edu

RM 32-D475B

Member

Geometric Data Processing Group

Sensible Deep Learning for 3D Data

https://people.csail.mit.edu/smirnov/

PI
Core/Dual

Justin Solomon

Assistant Professor

Geometry, graphics, vision, machine learning, optimization.

E jsolomon@mit.edu

RM

Lead

Computer Graphics Group

Geometric Data Processing Group

Geometry in Large-Scale Machine Learning

Political Geometry: Establishing Fair Mathematical Standards for Political Redistricting

Sensible Deep Learning for 3D Data

Optimal transport for statistics and machine learning

Geometry and topology for scientific computing and shape analysis

Member

Visual Computing Community of Research

Geometry, graphics, vision, machine learning, optimization.

PI
Core/Dual

Russ Tedrake

Professor

Robotics, machine learning, motion planning and control

E russt@csail.mit.edu

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

PI
Core/Dual

Antonio Torralba

Professor

Vision, graphics, perception, machine learning

E torralba@csail.mit.edu

T 324-0900

RM 32-386G

Lead

Understanding Light via Deep Neural Networks

Understandable Deep Networks

Understanding Food images

Member

Predictive Vision

Where Are They Looking?

Single-Image 3D Interpreter Network

VirtualHome: Representing Activities as Programs

Places Database for Scene Recognition

Semantic Scene Understanding through ADE20K dataset

Crossing the Vision-Language Boundary

Center for Deployable Machine Learning (CDML)

Visual Computing Community of Research

Embodied Intelligence Community of Research

Vision, graphics, perception, machine learning

20 News Results

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

Autonomous boats

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

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

m-blocks

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

point cloud 1

Algorithms & Theory AI & ML Graphics & Vision Big Data Transportation

October 29 '19

Deep learning with point clouds

Research aims to make it easier for self-driving cars, robotics, and other applications to understand the 3D world.

Deep learning with point clouds

Pool

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

Roboat2

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

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

Underwater_stock1

Algorithms & Theory AI & ML Robotics Transportation

February 01 '19

Engineers program marine robots to take calculated risks

Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.

Engineers program marine robots to take calculated risks

AI blind spot

Algorithms & Theory AI & ML Robotics Transportation

January 25 '19

Identifying artificial intelligence “blind spots”

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

Identifying artificial intelligence “blind spots”

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

model helps robots naviagte like humans 1

Algorithms & Theory AI & ML Robotics Transportation

October 04 '18

Model helps robots navigate more like humans do

In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.

Model helps robots navigate more like humans do

driverless cars 2

Algorithms & Theory AI & ML Robotics Transportation

May 23 '18

Making driverless cars change lanes more like human drivers do

Algorithm computes “buffer zones” around autonomous vehicles and reassess them on the fly.

Making driverless cars change lanes more like human drivers do

self driving

AI & ML Graphics & Vision Robotics Transportation

May 07 '18

Self-driving cars for country roads

For all the progress made in self-driving technologies, there still aren’t many places where they can actually drive. Companies like Google only test their fleets in major cities where they’ve spent countless hours meticulously labeling the exact 3-D positions of lanes, curbs, off-ramps, and stop signs.

Self-driving cars for country roads

Robotic fish

AI & ML Robotics Manufacturing Transportation

March 21 '18

Soft robotic fish swims alongside real ones in coral reefs

Made of silicone rubber, CSAIL’s “SoFi” could enable a closer study of aquatic life.

Soft robotic fish swims alongside real ones in coral reefs

drone1

Algorithms & Theory Robotics Energy Manufacturing Transportation

February 12 '18

Programming drones to fly in the face of uncertainty

CSAIL's NanoMap system enables drones to avoid obstacles while flying at 20 miles per hour, by more deeply integrating sensing and control.

Programming drones to fly in the face of uncertainty

Professor Berthold Horn and postdoctoral associate Liang Wang

Algorithms & Theory Graphics & Vision Transportation

December 14 '17

Improving traffic - by tailgating less

New CSAIL work shows that traffic would flow faster if drivers kept an equal distance between cars

Improving traffic - by tailgating less

ACM Fellows

Algorithms & Theory Robotics Security & Cryptography Cybersecurity Internet of Things Manufacturing

December 11 '17

Four from MIT named 2017 Association for Computer Machinery Fellows

Today four MIT faculty were named among the Association for Computer Machinery's 2017 Fellows for making “landmark contributions to computing.”

Four from MIT named 2017 Association for Computer Machinery Fellows

A new system from MIT's Computer Science and Artificial Intelligence Laboratory works by analyzing light at the edge of walls, which is impacted by the reflections of objects around the corner from the camera.

AI & ML Graphics & Vision Transportation

October 09 '17

An algorithm for your blind spot

Light lets us see the things that surround us, but what if we could also use it to see things hidden around corners? It sounds like science fiction, but that’s the idea behind a new algorithm out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) — and its discovery has implications for everything from emergency response to self-driving cars.

An algorithm for your blind spot

System directs camera-equipped drones to maintain framing of an aerial shot.

Algorithms & Theory AI & ML Graphics & Vision Robotics Entertainment Transportation

May 17 '17

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