SENSE.nano has announced the recipients of the third annual SENSE.nano seed grants. This year’s grants serve to advance innovations in sensing technologies for augmented and virtual realities (AR/VR) and advanced manufacturing systems. A center of excellence powered by MIT.nano, SENSE.nano received substantial interest in its 2019 call for proposals, making for stiff competition. Proposals were reviewed and evaluated by a committee consisting of industry and academia thought-leaders and were selected for funding following significant discussion. Ultimately, two projects were awarded $75,000 each to further research related to detecting movement in molecules and monitoring machine health. “SENSE.nano strives to convey the breadth and depth of sensing research at MIT,” says Brian Anthony, co-leader of SENSE.nano, associate director of MIT.nano, and a principal research scientist in the Department of Mechanical Engineering. “As we work to grow SENSE.nano’s research footing and to attract partners, it is encouraging to know that so much important research — Continue reading SENSE.nano awards seed grants in optoelectronics, interactive manufacturing
A little over a decade ago, researchers at the University of Tehran introduced a rudimentary humanoid robot called Surena. An improved model capable of walking, Surena II, was announced not long after, followed by the more capable Surena III in 2015. Now the Iranian roboticists have unveiled Surena IV. The new robot is a major improvement over previous designs. A video highlighting its capabilities shows the robot mimicking a person’s pose, grasping a water bottle, and writing its name on a whiteboard. Surena is also shown taking a group selfie with its human pals.
A version of this article was originally published on Medium. The views expressed here are solely those of the authors and do not represent positions of IEEE Spectrum or the IEEE. We here at Skydio have been developing and deploying machine learning systems for years due to their ability to scale and improve with data. However, to date our learning systems have only been used for interpreting information about the world; in this post, we present our first machine learning system for actually acting in the world. Using a novel learning algorithm, the Skydio autonomy engine, and only 3 hours of “off-policy” logged data, we trained a deep neural network pilot that is capable of filming and tracking a subject while avoiding obstacles.
A new generation of autonomous robots is helping plant breeders shape the crops of tomorrow. More details
In recent years, there has been a growing interest in using internet and mobile technology to increase access to the voting process. At the same time, computer security experts caution that paper ballots are the only secure means of voting. Now, MIT researchers are raising another concern: They say they have uncovered security vulnerabilities in a mobile voting application that was used during the 2018 midterm elections in West Virginia. Their security analysis of the application, called Voatz, pinpoints a number of weaknesses, including the opportunity for hackers to alter, stop, or expose how an individual user has voted. Additionally, the researchers found that Voatz’s use of a third-party vendor for voter identification and verification poses potential privacy issues for users. The findings are described in a new technical paper by Michael Specter, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS) and a member of Continue reading MIT researchers identify security vulnerabilities in voting app
For the first time, MIT researchers have enabled a soft robotic arm to understand its configuration in 3D space, by leveraging only motion and position data from its own “sensorized” skin. Soft robots constructed from highly compliant materials, similar to those found in living organisms, are being championed as safer, and more adaptable, resilient, and bioinspired alternatives to traditional rigid robots. But giving autonomous control to these deformable robots is a monumental task because they can move in a virtually infinite number of directions at any given moment. That makes it difficult to train planning and control models that drive automation. Traditional methods to achieve autonomous control use large systems of multiple motion-capture cameras that provide the robots feedback about 3D movement and positions. But those are impractical for soft robots in real-world applications. In a paper being published in the journal IEEE Robotics and Automation Letters, the researchers describe Continue reading “Sensorized” skin helps soft robots find their bearings
Quantum computing researchers have made improved qubits by exploiting concepts from high school chemistry. More details