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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.
Nvidia 's AI makes a breakthrough in ray tracing(computer graphics): Nvidia' AI that started out in 2017 as something that was not expected to do as much as it has already done like enriching graphics, transforming huge amounts of medical data into life saving breakthroughs, identifying diseases with a simple drop of blood, finding new ways to bring cures to the market faster, helping crops to flourish with optimal materials, customer assistance, self driving vehicles, analysis of various things in search for solutions etc. This AI became popular due to the Isaac robot simulator program. This got rid of programming and let the AI or robot think and learn on its own. Of course initially it was inefficient but after they found out that the robot could successfully learn something like hockey they wanted to make it efficient. This is where their idea of a virtual world comes in. They crated a program that takes the brain of a robot and puts it in a virtual world where it is allowed to try as much as it likes to achieve a certain task. This virtual world follows the laws of our world except for time. In this world a robot can practice its goal in very less time therefore becoming efficient. This AI has now made a breakthrough in computer graphics. ray tracing which is a method used for non real time instances due to its low computing speed has now been turned into a more faster computing one to handle real time gaming systems. Ray tracing is normally used to enhance effects bye understanding how our eye works, but was unable to handle on going instances like gaming and that was why it was limited to only movies.But now thanks to the AI and the quadro GV100 it is now possible to use get high quality graphics by using ray tracing for games as well. This also cuts the cost to 1/5 the original and takes 1/7 the original time taken. Issued by BTM layout robotic center
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,
Controlling robots with brainwaves and hand gestures Computer Science and Artificial Intelligence Laboratory system enable people to correct robot mistakes on multiple-choice tasks. Getting robots to do things isn’t easy, usually, scientists have to either explicitly program them or get them to understand how humans communicate via language. But what if we could control robots more intuitively, using just hand gestures and brainwaves? A new system spearheaded by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to do exactly that, allowing users to instantly correct robot mistakes with nothing more than brain signals and the flick of a finger. Building off the team’s past work focused on simple binary-choice activities, the new work expands the scope to multiple-choice tasks, opening up new possibilities for how human workers could manage teams of robots. By monitoring brain activity, the system can detect in real-time if a person notices an error as a robot does a task. Using an interface that measures muscle activity, the person can then make hand gestures to scroll through and select the correct option for the robot to execute. 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,
Flying Dragon Robot Transforms Itself to Squeeze Through Gaps. Dragon can change its shape to move through complex environments and even manipulate objects. There’s been a lot of recent focus on applications for aerial robots, and one of the areas with the most potential is indoors. The thing about indoors is that by definition you have to go through doors to get there, and once you’re inside, there are all kinds of things that are horribly dangerous to aerial robots, like more doors, walls, windows, people, furniture, hanging plants, lampshades, and other aerial robots, inevitably followed by still more doors. One solution is to make your robots super small, so that they can fit through small openings without running into something fragile and expensive, but then you’re stuck with small robots that can’t do a whole heck of a lot. Another solution is to put your robots in protective cages, but then you’re stuck with robots that can’t as easily interact with their environment, even if they want to. Ideally, you’d want a robot that doesn’t need that level of protection, that’s somehow large and powerful but also small and nimble at the same time. At JSK Lab at the University of Tokyo, roboticists have developed a robot called DRAGON, which (obviously) stands for for “Dual-rotor embedded multilink Robot with the Ability of multi-degree-of-freedom aerial transformation.” It’s a modular flying robot powered by ducted fans that can transform literally on the fly, from a square to a snake to anything in between, allowing it to stretch out to pass through small holes and then make whatever other shape you want once it’s on the other side. DRAGON is made of a series of linked modules, each of which consists of a pair of ducted fan thrusters that can be actuated in roll and pitch to vector thrust in just about any direction you need. The modules are connected to one another with a powered hinged joint, and the whole robot is driven by an Intel Euclid and powered by a battery pack (providing 3 minutes of flight time, which is honestly more than I would have thought), mounted along the robot’s spine. This particular prototype is made up of four modules, allowing it to behave sort of like a quad rotor, even though I suppose technically it’s an octorotor. 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,
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