The Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT) Editorial Board has awarded MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Gwangju Institute of Science and Technology (GIST) researchers with a Distinguished Paper Award for their evaluation of visual explanations in autonomous vehicles’ decision-making.

For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.

Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task.

Have you ever felt reluctant to share ideas during a meeting because you feared judgment from senior colleagues? You’re not alone. Research has shown this pervasive issue can lead to a lack of diversity in public discourse, especially when junior members of a community don’t speak up because they feel intimidated.

From wiping up spills to serving up food, robots are being taught to carry out increasingly complicated household tasks. Many such home-bot trainees are learning through imitation; they are programmed to copy the motions that a human physically guides them through.

Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people, and crosswalks, but almost no one can draw every detail with pixel-perfect accuracy. The same is true for most modern computer vision algorithms: They are fantastic at capturing high-level details of a scene, but they lose fine-grained details as they process information.

If a robot traveling to a destination has just two possible paths, it needs only to compare the routes’ travel time and probability of success. But if the robot is traversing a complex environment with many possible paths, choosing the best route amid so much uncertainty can quickly become an intractable problem.

Autonomous helicopters made by Rotor Technologies, a startup led by MIT alumni, take the human out of risky commercial missions.

An interdisciplinary team of researchers thinks health AI could benefit from some of the aviation industry’s long history of hard-won lessons that have created one of the safest activities today.