Research

  • Impact Areas
  • Research Areas

Algorithms & Theory

Foundational work that includes complexity, parallel computing and game theory

AI & ML

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

Computational Biology

Understanding disease via epigenomics, gene regulation and bioinformatics

Computer Architecture

How CPUs, memory and other systems are designed and organized

Graphics & Vision

Teaching computers to interpret, create and animate visual data

Human-Computer Interaction

Software and hardware that let us naturally interact with technology

Programming Languages & Software Engineering

From compilers and verification to software design and engineering

Robotics

Vision, actuation, sensing and manipulation of machines

Security & Cryptography

Developing technologies to prevent and recover from cyber-attacks

Systems & Networking

From distributed systems and databases to wireless

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1 Group Results matching all criteria

1 Project Results matching all criteria

Project

Safety Standards for Autonomous Vehicles

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

Leads

Lucas_Pic

Lucas Liebenwein

default headshot

Wilko Schwarting

Rus

Daniela Rus

Research Areas

Algorithms & Theory
Robotics

Impact Areas

Transportation

6 People Results matching all criteria

Russ Tedrake

PI
Core/Dual

Russ Tedrake

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

Lead

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

Member

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

PI
Core/Dual

Brian Williams

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

Lead

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

Member

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

16 News Results matching all criteria

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

Cinematography on the fly

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

Cinematography on the fly

1 Group Results

1 Project Results

Project

Safety Standards for Autonomous Vehicles

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

Leads

Lucas_Pic

Lucas Liebenwein

default headshot

Wilko Schwarting

Rus

Daniela Rus

Research Areas

Algorithms & Theory
Robotics

Impact Areas

Transportation

6 People Results

Russ Tedrake

PI
Core/Dual

Russ Tedrake

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

Lead

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

Member

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

PI
Core/Dual

Brian Williams

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

Lead

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

Member

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

16 News Results

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

Cinematography on the fly

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

Cinematography on the fly