“A team of researchers at the University of Zurich just announced that they've developed a drone software that's capable of identifying and following trails.”
Leave the breadcrumbs at home, folks, because just this week, a group of researchers in Switzerland announced the development of a drone capable of recognizing and following man-made forest trails. A collaborative effort between the University of Zurich and the Dalle Molle Institute of Artificial Intelligence, the conducted research was reportedly done to remedy the increasing number of lost hikers each year.
According to the University of Zurich, an estimated 1,000 emergency calls are made each year in regards to injured or lost hikers in Switzerland alone, an issue the group believes “inexpensive” drones could solve quickly.
Though the drone itself may get the bulk of the spotlight, it’s the artificial intelligence software developed by the partnership that deserves much of the credit. Run via a combination of AI algorithms, the software continuously scans its surroundings by way of two smartphone-like cameras built-in to the drone’s exterior. As the craft autonomously navigates a forested area, it consistently detects trails before piloting itself down open paths. However, the term “AI algorithms” is an incredibly easy way of describing something wildly complex. Before diving into the research, the team knew it would have to develop a supremely talented computing brain.
Instead of being programmed, a robot uses brain-inspired algorithms to “imagine” doing tasks before trying them in the real world.
Like many toddlers, Darwin sometimes looks a bit unsteady on its feet. But with each clumsy motion, the humanoid robot is demonstrating an important new way for androids to deal with challenging or unfamiliar environments. The robot learns to perform a new task by using a process somewhat similar to the neurological processes that underpin childhood learning.
Darwin lives in the lab of Pieter Abbeel, an associate professor at the University of California, Berkeley. When I saw the robot a few weeks ago, it was suspended from a camera tripod by a piece of rope, looking a bit tragic. A little while earlier, Darwin had been wriggling around on the end of the rope, trying to work out how best to move its limbs in order to stand up without falling over.
Darwin’s motions are controlled by several simulated neural networks—algorithms that mimic the way learning happens in a biological brain as the connections between neurons strengthen and weaken over time in response to input. The approach makes use of very complex neural networks, which are known as deep-learning networks, which have many layers of simulated neurons.
For the robot to learn how to stand and twist its body, for example, it first performs a series of simulations in order to train a high-level deep-learning network how to perform the task—something the researchers compare to an “imaginary process.” This provides overall guidance for the robot, while a second deep-learning network is trained to carry out the task while responding to the dynamics of the robot’s joints and the complexity of the real environment. The second network is required because when the first network tries, for example, to move a leg, the friction experienced at the point of contact with the ground may throw it off completely, causing the robot to fall.
The researchers had the robot learn to stand, to move its hand to perform reaching motions, and to stay upright when the ground beneath it tilts. “It practices in simulation for about an hour,” says Igor Mordatch, a postdoctoral researcher at UC Berkeley who carried out the study. “Then at runtime it’s learning on the fly how not to slip.”
“We’re trying to be able to deal with more variability,” says Abbeel. “Just even a little variability beyond what it was designed for makes it really hard to make it work.” The new technique could prove useful for any robot working in all sorts of real environments, but it might prove especially useful for more graceful legged locomotion.
The current approach is to design an algorithm that takes into account the dynamics of a process such as walking or running (see “The Robots Walking This Way”). But such models can struggle to deal with variation in the real world, as many of the humanoid robots involved in the DARPA Robotics Challenge demonstrated by falling over when walking on sand, or when unbalancing themselves by reaching out to grasp something (see “Why Robots, and Humans, Struggled with DARPA’s Challenge”). “It was a bit of a reality check,” Abbeel says. “That’s what happens in the real world.”
Dieter Fox, a professor in the computer science and engineering department at the University of Washington who specializes in robot perception and control, says neural network learning has huge potential in robotics. “I’m very excited about this whole research direction,” Fox says. “The problem is always if you want to act in the real world. Models are imperfect. Where machine learning, and especially deep learning comes in, is learning from the real-world interactions of the system.”
Researchers have demonstrated a display that lets audiences watch 3-D films in a theater without extra eyewear. Dubbed “Cinema 3D,” the MIT / Weizmann Institute of Science prototype uses lenses and mirrors to enable viewers to watch a 3-D movie from any seat.
MIT researchers have developed a new technique for imaging brain tissue at multiple scales, allowing them to image molecules within cells or take a wider view of the long-range connections between neurons. The technique, magnified analysis of proteome (MAP), should help scientists chart the connectivity and functions of neurons in the human brain.
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