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Nvidia is training robots to learn new skills by observing humans. Initial experiments with the process have seen a Baxter robot learn to pick up and move colored boxes and a toy car in a lab environment. The researchers hope the development of the new deep-learning based system will go some way to train robots to work alongside humans in both manufacturing and home settings. “In the manufacturing environment, robots are really good at repeatedly executing the same trajectory over and over again, but they don’t adapt to changes in the environment, and they don’t learn their tasks, ” Nvidia principal research scientist Stan Birchfield told VentureBeat. “So to repurpose a robot to execute a new task, you have to bring in an expert to reprogram the robot at a fairly low level, and it’s an expensive operation. What we’re interested in doing is making it easier for a non-expert user to teach a robot a new task by simply showing it what to do.” The researchers trained a sequence of neural networks to perform duties associated with perception, program generation, and program execution. The result was that the robot was able to learn a new task from a single demonstration in the real world. Once the robot witnesses the task, it generates a human-readable description of the states required to complete the task. A human can then correct the steps if necessary before execution on the real robot. “There’s sort of a paradigm shift happening in the robotics community now, ” Birchfield said. “We’re at the point now where we can use GPUs to generate essentially a limitless amount of pre-labeled data essentially for free to develop and test algorithms. And this is potentially going to allow us to develop these robotics systems that need to learn how to interact with the world around them in ways that scale better and are safer.” In a video released by the researchers, human operator shows a pair of stacks of cubes to the robot. The system then understands an appropriate program and correctly places the cubes in the correct order. Information gathered by - Robotics for u. Bangalore Robotics, BTM Robotics training center, Robotics spares, Bannerghatta Robotics training center, best robotics training in bangalore,
This Terrifying Robot Wolf is protecting the crops of Japanese Farmers For the last eight months, farms near Kisarazu City in Japan have been home to a horrifying robot wolf. But don’t worry; it wasn’t created to terrorize local residents (although, from the looks of the thing, it probably did). Its official name is “Super Monster Wolf, ” and engineers designed it to stop animals from eating farmers’ crops. In truth, the story of the robot wolf is more than a little sad. As Motherboard reports, wolves went extinct in Japan in the early 1800s. A state-sponsored eradication campaign. Now, parts of Japan are overrun with deer and wild boar. They love to feast on farmers’ rice and chestnut crops. Obviously, farmers do not love this. Fast forward 200 years and humans create a robotic wolf to replace the species they killed off. But there is some good here. The first official trial of the robot wolf just ended and surprised it was a resounding success. In fact, it was such a success that the wolf is entering mass production next month. Content gathered by BTM robotics training center, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP Nagar, robotics training centers in Bannerghatta road, robotics training centers in JP Nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore,
This Terrifying Robot Wolf is protecting the crops of Japanese Farmers. For the last eight months, farms near Kisarazu City in Japan have been home to a horrifying robot wolf. But don’t worry; it wasn’t created to terrorize local residents (although, from the looks of the thing, it probably did). Its official name is “Super Monster Wolf, ” and engineers designed it to stop animals from eating farmers’ crops. In truth, the story of the robot wolf is more than a little sad. As Motherboard reports, wolves went extinct in Japan in the early 1800s. A state-sponsored eradication campaign. Now, parts of Japan are overrun with deer and wild boar. They love to feast on farmers’ rice and chestnut crops. Obviously, farmers do not love this. Fast forward 200 years and humans create a robotic wolf to replace the species they killed off. But there is some good here. The first official trial of the robot wolf just ended and surprised it was a resounding success. In fact, it was such a success that the wolf is entering mass production next month. Content gathered by BTM robotics training center, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP nagar, robotics training centers in Bannerghatta road, robotics training centers in JP nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
Sony revives Aibo robot dog after 11 years Sony is to breathe new life into its Aibo robotic dog, releasing a new - and much cuter - version more than a decade after shelving its original line of tech pets. The Japanese robot-dog can respond to human actions and voice commands and bark, sit and wag its tail and play. Aibo can also learn actions that keep its users happy, while users will be able to connect the dog to the cloud to let it learn further actions from other Aibo dogs. Sony's original Aibo was one of the first artificial intelligence products built for ordinary consumers, first released in 1999. Aibo, a meaning partner in Japanese (but which neatly also stands for AI bot), had new models released every year until 2005. The futuristic family pet preceded smartphones and apps and was used both as a domestic toy and in research projects exploring human and AI interaction. The original featured lifelike, if slightly slow and clunky, movements and used computer vision to interact with the world around it. However, Sony ultimately stopped production of Aibo in 2006 after it was forced to slash its product line-up in a cost-cutting exercise. But the robot remained an early icon of future homes, and Sony briefly revived the dog for use in its Xperia phone marketing campaigns. Aibo will have OLED eyes, stand a foot tall, weigh two kilograms, have a two-hour battery life and start at 198, 000 Yen (£1, 300). It will also set users back a monthly subscription fee worth around £20 per month. Aibo will only be available in Japan. Content gathered by BTM robotics training centre, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP Nagar, robotics training centres in Bannerghatta road, robotics training centres in JP Nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
Robot can pick up any object after inspecting it. Humans have long been masters of dexterity, a skill that can largely be credited to the help of our eyes. Robots, meanwhile, are still catching up. Certainly there's been some progress: for decades robots in controlled environments like assembly lines have been able to pick up the same object over and over again. More recently, breakthroughs in computer vision have enabled robots to make basic distinctions between objects, but even then, they don't truly understand objects' shapes, so there's little they can do after a quick pick-up. In a new paper, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), say that they've made a key development in this area of work: a system that lets robots inspect random objects, and visually understand them enough to accomplish specific tasks without ever having seen them before. The system, dubbed "Dense Object Nets" (DON), looks at objects as collections of points that serve as "visual roadmaps" of sorts. This approach lets robots better understand and manipulate items, and, most importantly, allows them to even pick up a specific object among a clutter of similar objects—a valuable skill for the kinds of machines that companies like Amazon and Walmart use in their warehouses. For example, someone might use DON to get a robot to grab onto a specific spot on an object—say, the tongue of a shoe. From that, it can look at a shoe it has never seen before, and successfully grab its tongue. "Many approaches to manipulation can't identify specific parts of an object across the many orientations that object may encounter, " says Ph.D. student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow Ph.D. student Pete Florence, alongside MIT professor Russ Tedrake. "For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side." The team views potential applications not just in manufacturing settings, but also in homes. Imagine giving the system an image of a tidy house, and letting it clean while you're at work, or using an image of dishes so that the system puts your plates away while you're on vacation. What's also noteworthy is that none of the data was actually labeled by humans; rather, the system is "self-supervised, " so it doesn't require any human annotations. Two common approaches to robot grasping involve either task-specific learning, or creating a general grasping algorithm. These techniques both have obstacles: task-specific methods are difficult to generalize to other tasks, and general grasping doesn't get specific enough to deal with the nuances of particular tasks, like putting objects in specific spots. The DON system, however, essentially creates a series of coordinates on a given object, which serve as a kind of "visual roadmap" of the objects, to give the robot a better understanding of what it needs to grasp, and where. The team trained the system to look at objects as a series of points that make up a larger coordinate system. It can then map different points together to visualize an object's 3-D shape, similar to how panoramic photos are stitched together from multiple photos. After training, if a person specifies a point on a object, the robot can take a photo of that object, and identify and match points to be able to then pick up the object at that specified point. This is different from systems like UC-Berkeley's DexNet, which can grasp many different items, but can't satisfy a specific request. Imagine an infant at 18-months old, who doesn't understand which toy you want it to play with but can still grab lots of items, versus a four-year old who can respond to "go grab your truck by the red end of it." In one set of tests done on a soft caterpillar toy, a Kuka robotic arm powered by DON could grasp the toy's right ear from a range of different configurations. This showed that, among other things, the system has the ability to distinguish left from right on symmetrical objects. When testing on a bin of different baseball hats, DON could pick out a specific target hat despite all of the hats having very similar designs—and having never seen pictures of the hats in training data before. "In factories robots often need complex part feeders to work reliably, " says Manuelli. "But a system like this that can understand objects' orientations could just take a picture and be able to grasp and adjust the object accordingly." In the future, the team hopes to improve the system to a place where it can perform specific tasks with a deeper understanding of the corresponding objects, like learning how to grasp an object and move it with the ultimate goal of say, cleaning a desk. The team will present their paper on the system next month at the Conference on Robot Learning in Zürich, Switzerland. Content gathered by BTM robotics training center, robotics in Bangalore, stem education in Bangalore, stem education in Bannerghatta road, stem education in JP Nagar, robotics training centres in Bannerghatta road, robotics training centres in JP Nagar, robotics training for kids, robotics training for beginners, best robotics in Bangalore.
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