By Mary Beth O’Leary
With the push of a button, months of hard work were about to be put to the test. Sixteen teams of engineers convened in a cavernous exhibit hall in Nagoya, Japan, for the 2017 Amazon Robotics Challenge. The robotic systems they built were tasked with removing items from bins and placing them into boxes. For graduate student Maria Bauza, who served as task-planning lead for the MIT-Princeton Team, the moment was particularly nerve-wracking.
The future of transportation in waterway-rich cities such as Amsterdam, Bangkok, and Venice — where canals run alongside and under bustling streets and bridges — may include autonomous boats that ferry goods and people, helping clear up road congestion.
In the field of self-driving cars, algorithms for controlling lane changes are an important topic of study. But most existing lane-change algorithms have one of two drawbacks: Either they rely on detailed statistical models of the driving environment, which are difficult to assemble and too complex to analyze on the fly; or they’re so simple that they can lead to impractically conservative decisions, such as never changing lanes at all.
Uber’s recent self-driving car fatality underscores the fact that the technology is still not ready for widespread adoption. The reality is that there aren’t many places where today’s self-driving cars can actually reliably 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, and stop signs.
If you were to ask someone to name a new technology that emerged from MIT in the 21st century, there’s a good chance they would name the robotic cheetah. Developed by the MIT Department of Mechanical Engineering’s Biomimetic Robotics Lab under the direction of Associate Professor Sangbae Kim, the quadruped MIT Cheetah has made headlines for its dynamic legged gait, speed, jumping ability, and biomimetic design.
If you’re a rock climber, hiker, runner, dancer, or anyone who likes recording themselves while in motion, a personal drone companion can now do all the filming for you — completely autonomously.
Skydio, a San Francisco-based startup founded by three MIT alumni, is commercializing an autonomous video-capturing drone — dubbed by some as the “selfie drone” — that tracks and films a subject, while freely navigating any environment.
Today, when an enterprise wants to use machine learning to solve a problem, they have to call in the cavalry. Even a simple problem requires multiple data scientists, machine learning experts, and domain experts to come together to agree on priorities and exchange data and information.
Unpacking groceries is a straightforward albeit tedious task: You reach into a bag, feel around for an item, and pull it out. A quick glance will tell you what the item is and where it should be stored.