Video Friday: Deep-Learning Robots, DRC Practice, and Drone Pilot Competition
Apologies for the light posting this week: the entire IEEE Spectrum team (both digital and print) was closeted away in meetings working on ways to better serve you, dear reader. Did we come up with some? Sure we did, but for now, they’re secret until we get them to work.
Leading the video news for today is research from UC Berkeley focused on teaching robots to learn tasks in ways that can be adapted to new situations, using a deep learning approach based on neural nets. The upshot is that it enables robots (like Berkeley’s PR2, named BRETT) to learn new tasks in a matter of hours and perform those tasks generally independently of their environment, all with a minimal amount of sensors.
This is stupendously important in two ways: first, it means that robots get significantly easier to teach, as opposed to requiring programming. And second, it means that robots are able to do useful stuff in useful environments, like your house as opposed to a robotics lab. Watch BRETT do his thing, and all the rest of our videos, starting right now.