A photorealistic simulation system enables autonomous vehicles to learn to drive in the real world and recover from near-crash scenarios.
MIT helping robots perform complex tasks without many rules
Training interactive robots may one day be an easy job for everyone, even those without programming expertise. Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees,…
‘Sensorized’ skin enables soft robotic arm to feel its own way at MIT
A ‘sensorized’ soft robotic arm combines soft sensors and machine learning to know where it is in a 3D environment.
Stretchy robots get new MIT model to optimize design, controls
Because they can move in multiple dimensions, optimizing soft robots to perform complex tasks is a huge computational challenge, but a new MIT model can help
‘Learning-in-the-loop’ method optimizes control of soft robots
MIT researchers have invented a way to efficiently optimize the control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation. Soft robots have springy, flexible, stretchy bodies that can essentially move an infinite number of ways at any given moment. Computationally, this represents a highly complex “state…
Merging at tricky intersections gets easier for self-driving cars with MIT, TRI model
MIT and Toyota researchers have developed a model to alert driverless cars when it’s safest to merge into traffic at intersections with obstructed views.
MIT ShadowCam helps autonomous vehicles see around corners
To improve the safety of autonomous systems, MIT engineers have developed a system that can sense tiny changes in shadows on the ground to determine if there’s a moving object coming around the corner. Autonomous cars could one day use the system to quickly avoid a potential collision with another car or pedestrian emerging from…
Omnipush dataset teaches robots how to push objects
Researchers at the Massachusetts Institute of Technology have compiled a dataset that captures the detailed behavior of a robotic system physically pushing hundreds of different objects. Using the dataset — the largest and most diverse of its kind — researchers can train robots to “learn” pushing dynamics that are fundamental to many complex object-manipulation tasks,…
‘Roboats’ from MIT can now autonomously change configurations
MIT researchers have added capabilities to ‘roboats,’ allowing them to form pop-up bridges, stages, and other structures.
Automated system from MIT generates robotic actuators for novel tasks
When designing actuators involves too many variables for humans to test by hand, this system can step in.
Photonic chip could run optical neural networks 10 million times more efficiently
MIT researchers have developed a novel “photonic” chip that uses light instead of electricity — and consumes relatively little power in the process. The chip could be used to process massive neural networks millions of times more efficiently than today’s classical computers do. Neural networks are machine-learning models that are widely used for such tasks…
Roboats, autonomous boats from MIT and AMS Institute, can connect for different applications
MIT and the Advanced Metropolitan Solutions Institute in Amsterdam have developed “roboats,” autonomous boats that can connect for a variety of uses. Researchers are scaling up their designs to be more stable.
Bringing human-like reasoning to autonomous vehicles
With aims of bringing more human-like reasoning to autonomous vehicles, MIT researchers have created a system that uses only simple maps and visual data to enable driverless cars to navigate routes in new, complex environments. Human drivers are exceptionally good at navigating roads they haven’t driven on before, using observation and simple tools. We simply…
Giving robots a better feel for object manipulation
A new learning system developed by MIT researchers improves robots’ abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch – and it may have fun applications in personal robotics, such as…
On-chip sub-terahertz system gives autonomous vehicles keener ‘eyesight’
Autonomous vehicles relying on light-based image sensors often struggle to see through blinding conditions, such as fog. But MIT researchers have developed a sub-terahertz-radiation receiving system that could help steer driverless cars when traditional methods fail. Sub-terahertz wavelengths, which are between microwave and infrared radiation on the electromagnetic spectrum, can be detected through fog and…
Helping computers perceive human emotions
Personalized machine-learning models capture subtle variations in facial expressions to better gauge how we feel. MIT Media Lab researchers have developed a machine-learning model that takes computers a step closer to interpreting our emotions as naturally as humans do. In the growing field of “affective computing,” robots and computers are being developed to analyze facial…