Posted: 11 Feb 2018 09:00 PM PST
Companies like Amazon have big ideas for drones that can deliver packages right to your door. But even putting aside the policy issues, programming drones to fly through cluttered spaces like cities is difficult. Being able to avoid obstacles while traveling at high speeds is computationally complex, especially for small drones that are limited in how much they can carry onboard for real-time processing.
Many existing approaches rely on intricate maps that aim to tell drones exactly where they are relative to obstacles, which isn't particularly practical in real-world settings with unpredictable objects. If their estimated location is off by even just a small margin, they can easily crash.
With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed NanoMap, a system that allows drones to consistently fly 20 miles per hour through dense environments such as forests and warehouses.
One of NanoMap's key insights is a surprisingly simple one: The system considers the drone's position in the world over time to be uncertain, and actually models and accounts for that uncertainty.
"Overly confident maps won't help you if you want drones that can operate at higher speeds in human environments," says graduate student Pete Florence, lead author on a new related paper. "An approach that is better aware of uncertainty gets us a much higher level of reliability in terms of being able to fly in close quarters and avoid obstacles."
Specifically, NanoMap uses a depth-sensing system to stitch together a series of measurements about the drone's immediate surroundings. This allows it to not only make motion plans for its current field of view, but also anticipate how it should move around in the hidden fields of view that it has already seen.
"It's kind of like saving all of the images you've seen of the world as a big tape in your head," says Florence. "For the drone to plan motions, it essentially goes back into time to think individually of all the different places that it was in."
The team's tests demonstrate the impact of uncertainty. For example, if NanoMap wasn't modeling uncertainty and the drone drifted just 5 percent away from where it was expected to be, the drone would crash more than once every four flights. Meanwhile, when it accounted for uncertainty, the crash rate reduced to 2 percent.
The paper was co-written by Florence and MIT Professor Russ Tedrake alongside research software engineers John Carter and Jake Ware. It was recently accepted to the IEEE International Conference on Robotics and Automation, which takes place in May in Brisbane, Australia.
For years computer scientists have worked on algorithms that allow drones to know where they are, what's around them, and how to get from one point to another. Common approaches such as simultaneous localization and mapping (SLAM) take raw data of the world and convert them into mapped representations.
But the output of SLAM methods aren't typically used to plan motions. That's where researchers often use methods like "occupancy grids," in which many measurements are incorporated into one specific representation of the 3-D world.
The problem is that such data can be both unreliable and hard to gather quickly. At high speeds, computer-vision algorithms can't make much of their surroundings, forcing drones to rely on inexact data from the inertial measurement unit (IMU) sensor, which measures things like the drone's acceleration and rate of rotation.
The way NanoMap handles this is that it essentially doesn't sweat the minor details. It operates under the assumption that, to avoid an obstacle, you don't have to take 100 different measurements and find the average to figure out its exact location in space; instead, you can simply gather enough information to know that the object is in a general area.
"The key difference to previous work is that the researchers created a map consisting of a set of images with their position uncertainty rather than just a set of images and their positions and orientation," says Sebastian Scherer, a systems scientist at Carnegie Mellon University's Robotics Institute. "Keeping track of the uncertainty has the advantage of allowing the use of previous images even if the robot doesn't know exactly where it is and allows in improved planning."
Florence describes NanoMap as the first system that enables drone flight with 3-D data that is aware of "pose uncertainty," meaning that the drone takes into consideration that it doesn't perfectly know its position and orientation as it moves through the world. Future iterations might also incorporate other pieces of information, such as the uncertainty in the drone's individual depth-sensing measurements.
NanoMap is particularly effective for smaller drones moving through smaller spaces, and works well in tandem with a second system that is focused on more long-horizon planning. (The researchers tested NanoMap last year in a program tied to the Defense Advanced Research Projects Agency, or DARPA.)
The team says that the system could be used in fields ranging from search and rescue and defense to package delivery and entertainment. It can also be applied to self-driving cars and other forms of autonomous navigation.
"The researchers demonstrated impressive results avoiding obstacles and this work enables robots to quickly check for collisions," says Scherer. "Fast flight among obstacles is a key capability that will allow better filming of action sequences, more efficient information gathering and other advances in the future."
This work was supported in part by DARPA's Fast Lightweight Autonomy program.
Posted: 11 Feb 2018 08:59 PM PST
Three commercially released facial-analysis programs from major technology companies demonstrate both skin-type and gender biases, according to a new paper researchers from MIT and Stanford University will present later this month at the Conference on Fairness, Accountability, and Transparency.
In the researchers' experiments, the three programs' error rates in determining the gender of light-skinned men were never worse than 0.8 percent. For darker-skinned women, however, the error rates ballooned — to more than 20 percent in one case and more than 34 percent in the other two.
The findings raise questions about how today's neural networks, which learn to perform computational tasks by looking for patterns in huge data sets, are trained and evaluated. For instance, according to the paper, researchers at a major U.S. technology company claimed an accuracy rate of more than 97 percent for a face-recognition system they'd designed. But the data set used to assess its performance was more than 77 percent male and more than 83 percent white.
"What's really important here is the method and how that method applies to other applications," says Joy Buolamwini, a researcher in the MIT Media Lab's Civic Media group and first author on the new paper. "The same data-centric techniques that can be used to try to determine somebody's gender are also used to identify a person when you're looking for a criminal suspect or to unlock your phone. And it's not just about computer vision. I'm really hopeful that this will spur more work into looking at [other] disparities."
Buolamwini is joined on the paper by Timnit Gebru, who was a graduate student at Stanford when the work was done and is now a postdoc at Microsoft Research.
The three programs that Buolamwini and Gebru investigated were general-purpose facial-analysis systems, which could be used to match faces in different photos as well as to assess characteristics such as gender, age, and mood. All three systems treated gender classification as a binary decision — male or female — which made their performance on that task particularly easy to assess statistically. But the same types of bias probably afflict the programs' performance on other tasks, too.
Indeed, it was the chance discovery of apparent bias in face-tracking by one of the programs that prompted Buolamwini's investigation in the first place.
Several years ago, as a graduate student at the Media Lab, Buolamwini was working on a system she called Upbeat Walls, an interactive, multimedia art installation that allowed users to control colorful patterns projected on a reflective surface by moving their heads. To track the user's movements, the system used a commercial facial-analysis program.
The team that Buolamwini assembled to work on the project was ethnically diverse, but the researchers found that, when it came time to present the device in public, they had to rely on one of the lighter-skinned team members to demonstrate it. The system just didn't seem to work reliably with darker-skinned users.
Curious, Buolamwini, who is black, began submitting photos of herself to commercial facial-recognition programs. In several cases, the programs failed to recognize the photos as featuring a human face at all. When they did, they consistently misclassified Buolamwini's gender.
To begin investigating the programs' biases systematically, Buolamwini first assembled a set of images in which women and people with dark skin are much better-represented than they are in the data sets typically used to evaluate face-analysis systems. The final set contained more than 1,200 images.
Next, she worked with a dermatologic surgeon to code the images according to the Fitzpatrick scale of skin tones, a six-point scale, from light to dark, originally developed by dermatologists as a means of assessing risk of sunburn.
Then she applied three commercial facial-analysis systems from major technology companies to her newly constructed data set. Across all three, the error rates for gender classification were consistently higher for females than they were for males, and for darker-skinned subjects than for lighter-skinned subjects.
For darker-skinned women — those assigned scores of IV, V, or VI on the Fitzpatrick scale — the error rates were 20.8 percent, 34.5 percent, and 34.7. But with two of the systems, the error rates for the darkest-skinned women in the data set — those assigned a score of VI — were worse still: 46.5 percent and 46.8 percent. Essentially, for those women, the system might as well have been guessing gender at random.
"To fail on one in three, in a commercial system, on something that's been reduced to a binary classification task, you have to ask, would that have been permitted if those failure rates were in a different subgroup?" Buolamwini says. "The other big lesson ... is that our benchmarks, the standards by which we measure success, themselves can give us a false sense of progress."
"This is an area where the data sets have a large influence on what happens to the model," says Ruchir Puri, chief architect of IBM's Watson artificial-intelligence system. "We have a new model now that we brought out that is much more balanced in terms of accuracy across the benchmark that Joy was looking at. It has a half a million images with balanced types, and we have a different underlying neural network that is much more robust."
"It takes time for us to do these things," he adds. "We've been working on this roughly eight to nine months. The model isn't specifically a response to her paper, but we took it upon ourselves to address the questions she had raised directly, including her benchmark. She was bringing up some very important points, and we should look at how our new work stands up to them."
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