This fall’s new FAA regulations have made drone flight easier than ever for both companies and consumers. But what if the drones out on the market aren’t exactly what you want?
A new system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is the first to allow users to design, simulate and build their own custom drone. Users can change the size, shape and structure of their drone based on the specific needs they have for payload, cost, flight time, battery usage and other factors.
Using ethorobotics, researchers from the BioRobotics Institute and the Zoology Institute of Bonn University published a novel ‘dummy fish’ to study the social behavior of weakly-electric fish Mormyrus rume (Boulenger) (Osteoglossiformes: Mormyridae).
There have been few postings this month, as I took the time to enjoy a holiday in New Zealand around speaking at the SingularityU New Zealand summit in Christchurch. The night before the summit, we enjoyed a 7.8 earthquake not so far from Christchurch, whose downtown was 2/3 demolished after quakes in 2010 and 2011. On the 11th floor of the hotel it was a disturbing nailbiter of swaying back and forth for over 2 minutes — but of course, swaying is what the building is supposed to do, that means it’s working. The shocks were rolling, not violent, and in fact, we got more violent jolts from aftershocks a week later when we went to Picton.
MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.
Vidi Systems from Switzerland is the overall Grand Winner of the 2016 Robot Launch global startup competition, beating out many US contestants in a field that included sensors, artificial intelligence, social robots, service robots and industrial solutions. Overall, the European robotics startups performed very strongly this year with 8 making The Shortlist for awards. Canada also had good representation with 3 entries, but the rest of The Shortlist were based in the USA, even if they had originated in Israel or Hong Kong.
U.S. Sen. Ted Cruz (R-Texas), chairman of the Subcommittee on Space, Science, and Competitiveness will convene a hearing today, 30 November, at 2:30 p.m. EST on “The Dawn of Artificial Intelligence.” The hearing will conduct a broad overview of the state of artificial intelligence, including policy implications and effects on commerce.
For #GivingTueday, we would like to ask you, our loyal readers, to consider donating to Robohub. From the beginning, our mission has been to help demystify robotics by hearing straight from the experts. Robohub isn’t like most news websites. We’re a community. We’re a forum. Much of our content is written directly by the experts in academia, businesses, and industry. That means you get to learn about the latest research and business news, events and opinions, directly from the experts, unfiltered, with no media bias. Our goal is to keep you engaged and interested in robotics that may not necessarily be covered by top news agencies.
How can robotics help to enhance the development of the modern arts? Japan’s famous playwright, stage director Oriza Hirata and leading roboticist Hiroshi Ishiguro launched the “Robot Theater Project” at Osaka University to explore the boundary between human-robot interactions through robot theater. Their work includes renditions of Anton Chekhov’s “Three Sisters”, Franz Kafka’s “The Metamorphosis”, and their own play “I, Worker”. Their work has spread internationally to Paris, New York, Toronto and Taipei.
For this interview, we would like to invite their collaboration partner Yi-Wei Keng, director of Taipei Arts Festival, to share his insights on the intersection of robotics and the arts.
Ethernet is the most pervasive communication standard in the world. However, it is often dismissed for robotics applications because of its presumed non-deterministic behavior. In this article, we show that in practice Ethernet can be extremely deterministic and provide a flexible and reliable solution for robot communication.
What ethical issues do we face in providing robot care for the elderly? Is there better acceptance with the public? What should we be mindful of when designing human-robot interactions?
At the #ERW2016 central event, held in Amsterdam 18-22 November, these questions (and more) were discussed, debated, and encouraged by expert panellists hailing from research, industry, academia, and government as well as insightful members in the community. All were welcome to ‘Robots at Your Service’, a multi-track event featuring panel deliberations in robotics regulation, assistive living technologies, and aimed at attracting more youth, and especially girls, into science, technology, engineering, arts and maths (STEAM). The event hosted workshops and featured a 48-hour hackathon for designers, makers, coders, engineers, and anyone else who believed healthy ageing should be a societal challenge.
A U.S. drone strike in Syria killed Abu Afghan al-Masri, a senior leader of al-Qaeda. Pentagon spokesperson Peter Cook confirmed that the strike took place near the town of Sarmada, in Aleppo province, on November 18. (Voice of America)
The National Transportation Safety Board is investigating an accident involving Facebook’s Aquila prototype drone during a test flight in June. According to an NTSB spokesperson, the drone experienced “structural failure” during the test flight. The Aquila is a high-altitude long-endurance drone that Facebook plans to use to beam Internet to remote areas. (Wall Street Journal)
The U.K. Civil Aviation Authority has revised the wording of its flight rules for drones. The move is part of the CAA’s push to increase awareness around responsible drone use. There have been several reported close encounters between drones and manned aircraft in U.K. airspace in recent months. (BBC)
Email chains released by the U.K. Civil Aviation Authority show that Amazon began testing delivery drones at a secret site at least a year earlier than previously thought. The chain refers to tests conducted as early as the summer of 2015, but it was only publicly revealed that the program had begun in summer 2016. (Business Insider)
The Swiss Society for Rescue Dogs is teaming up with the Swiss Federation of Civil Drones to use unmanned aircraft during search operations. (The Local)
Drone maker Autel robotics published a video showing how a drone can be used to help prepare a Thanksgiving meal. (Gizmodo)
The U.S. Air Force awarded General Atomics Aeronautical Systems a $39.8 million contract modification to extend the range on the MQ-9 Reaper. (Contract Announcement)
Kratos Defense & Security Solutions announced that it had been awarded a $17.8 million contract for BQM-167i target drones from an unidentified international customer. (Shephard Media)
The U.S. Navy awarded Northrop Grumman a $10.4 million contract to increase production of the MQ-8C Fire Scout. (FBO)
The Department of the Interior awarded 3D Robotics a $5,081 contract for unmanned aircraft systems. (Contract Announcement)
The European Maritime Safety Agency awarded Martek Marine a $10.6 million contract for drones that will monitor marine pollution levels. (BBC)
GoPro is offering free Hero 5 sport cameras and full refunds to customers who bought the Karma drone before the recall. (Investopedia)
For updates, news, and commentary, follow us on Twitter. The Weekly Drone Roundup is a newsletter from the Center for the Study of the Drone. It covers news, commentary, analysis and technology from the drone world. You can subscribe to the Roundup here.
Living in a dynamic physical world, it’s easy to forget how effortlessly we understand our surroundings. With minimal thought, we can figure out how scenes change and objects interact.
But what’s second nature for us is still a huge problem for machines. With the limitless number of ways that objects can move, teaching computers to predict future actions can be difficult.
Recently, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have gotten a step closer, developing a deep-learning algorithm that, given still images from a scene, can create brief videos that simulate the future of that scene.
Trained on two million unlabeled videos that include a year’s worth of footage, the algorithm generated videos that human subjects deemed to be realistic 20 percent more often than a baseline model.
To be clear, at this point the videos are still relatively low-resolution and only 1-1.5 seconds in length. But the team is hopeful that future versions could be used for everything from improved security tactics to safer self-driving cars.
According to CSAIL PhD student and first author Carl Vondrick, the algorithm can also help machines recognize people’s activities without expensive human annotations.
“These videos show us what computers think can happen in a scene,” says Vondrick. “If you can predict the future, you must have understood something about the present.”
Vondrick wrote the paper with MIT professor Antonio Torralba and Hamed Pirsiavash, a former CSAIL postdoctoral associate who is now a professor at the University of Maryland, Baltimore County. The work will be presented at next week’s Neural Information Processing Systems (NIPS) conference in Barcelona.
How it works Multiple researchers have tackled similar topics in computer vision, including MIT professor Bill Freeman, whose new work on “visual dynamics” also creates future frames in a scene. But where his model focuses on extrapolating videos into the future, Torralba’s model can also generate completely new videos that haven’t been seen before.
Previous systems build up scenes frame by frame, which creates a large margin for error. In contrast, this work focuses on processing the entire scene at once, with the algorithm generating as many as 32 frames from scratch per second.
“Building up a scene frame-by-frame is like a big game of ‘Telephone,’ which means that the message falls apart by the time you go around the whole room,” says Vondrick. “By instead trying to predict all frames simultaneously, it’s as if I’m talking to everyone in the room at once.”
Of course, there’s a trade-off to generating all frames simultaneously: while it becomes more accurate, the computer model also becomes more complex for longer videos.
To create multiple frames, researchers taught the model to generate the foreground separate from the background, and to then place the objects in the scene to let the model learn which objects move and which objects don’t.
The team used a deep-learning method called “adversarial learning” that involves training two competing neural networks. One network generates video, and the other discriminates between the real and generated videos. Over time, the generator learns to fool the discriminator.
From that, the model can create videos resembling scenes from beaches, train stations, hospitals, and golf courses. For example, the beach model produced beaches with crashing waves, and the golf model had people walking on grass.
Testing the scene The team compared the videos against a baseline of generated videos and asked subjects which they thought were more realistic. From over 13,000 opinions of 150 users, subjects chose the generative model videos 20 percent more often than the baseline.
To be clear, the the model still lacks some fairly simple common-sense principles. For example, it often doesn’t understand that objects are still there when they move, like when a train passes through a scene. The model also tends to make humans and objects look much larger in size than reality.
As mentioned before, another limitation is that the generated videos are just one and a half seconds long, which the team hopes to be able to increase in future work. The challenge is that this requires tracking longer dependencies to ensure that the scene still makes sense over longer time periods. One way to do this would be to add human supervision.
“It’s difficult to aggregate accurate information across long time periods in videos,” says Vondrick. “If the video has both cooking and eating activities, you have to be able to link those two together to make sense of the scene.”
These types of models aren’t limited to predicting the future. Generative videos can be used for adding animation to still images, like the animated newspaper from the Harry Potter books. They could also help detect anomalies in security footage and compress data for storing and sending longer videos.
“In the future, this will let us scale up vision systems to recognize objects and scenes without any supervision, simply by training them on video,” says Vondrick.
This work was supported by the National Science Foundation, the START program at UMBC, and a Google PhD fellowship.