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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,
Look, up in the sky! It's Disney's new autonomous acrobatic robot. Disney's animatronics are coming a long way from drunken pirates waving flagons of ale or hippos that wiggle their ears. In the (relatively) near future, robotic versions of Iron Man or Buzz Lightyear could be performing autonomous acrobatics overhead in Disney theme parks, thanks to the newly-unveiled Stuntronics robot. Animatronic characters have populated Disney parks for more than half a century, albeit often just looping a specific movement over and over. In recent years Disney Research has tried to make the robots more agile and interactive, developing versions that can grab objects more naturally and even juggle and play catch with visitors. Back in May, the company unveiled a prototype called Stickman. Basically a mechanical stick with two degrees of freedom, the robot could be flicked into the air like a trapeze artist, where it used a suite of sensors to tuck and roll in midair, perform a couple of backflips, and unfurl for landing. Impressive as that is, Stickman was far more stick than man. In just a few short months, the project has evolved into Stuntronics, a robot that's noticeably more human. Designed to be a kind of robotic stunt double for a human actor, the Stuntronics robot can perform the same kind of autonomous aerial stunts thanks to a similar load of sensors as Stickman, including an accelerometer, gyroscope array and laser range finding. But unlike Stickman, Stuntronics can stick its landing too. The former bot tended to land flat on its back, but the new version can land feet-first, and hit what looks like a smaller target. Not only that, it can strike a heroic pose in the air, before tucking back up ready for landing. Disney Research scientists said that during a stage show or ride, other animatronics or human actors could perform the up-close, static scenes before the Stuntronics robot is wheeled out when the character needs to fly (or fall with style). Of course, there's no guarantee that this kind of thing will ever get off the ground (literally or figuratively), but it's always exciting to peek behind the curtain at Disneyland. 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,
'Blind' Cheetah 3 robot can climb stairs littered with obstacles. MIT's Cheetah 3 robot can now leap and gallop across rough terrain, climb a staircase littered with debris, and quickly recover its balance when suddenly yanked or shoved, all while essentially blind. The 90-pound mechanical beast -- about the size of a full-grown Labrador -- is intentionally designed to do all this without relying on cameras or any external environmental sensors. Instead, it nimbly "feels" its way through its surroundings in a way that engineers describe as "blind locomotion, " much like making one's way across a pitch-black room. "There are many unexpected behaviours the robot should be able to handle without relying too much on vision, " says the robot's designer, Sangbae Kim, associate professor of mechanical engineering at MIT. "Vision can be noisy, slightly inaccurate, and sometimes not available, and if you rely too much on vision, your robot has to be very accurate in position and eventually will be slow. So we want the robot to rely more on tactile information. That way, it can handle unexpected obstacles while moving fast." Researchers will present the robot's vision-free capabilities in October at the International Conference on Intelligent Robots, in Madrid. In addition to blind locomotion, the team will demonstrate the robot's improved hardware, including an expanded range of motion compared to its predecessor Cheetah 2, that allows the robot to stretch backwards and forwards, and twist from side to side, much like a cat limbering up to pounce. Within the next few years, Kim envisions the robot carrying out tasks that would otherwise be too dangerous or inaccessible for humans to take on. "Cheetah 3 is designed to do versatile tasks such as power plant inspection, which involves various terrain conditions including stairs, curbs, and obstacles on the ground, " Kim says. "I think there are countless occasions where we [would] want to send robots to do simple tasks instead of humans. Dangerous, dirty, and difficult work can be done much more safely through remotely controlled robots." Making a commitment The Cheetah 3 can blindly make its way up staircases and through unstructured terrain, and can quickly recover its balance in the face of unexpected forces, thanks to two new algorithms developed by Kim's team: a contact detection algorithm, and a model-predictive control algorithm. The contact detection algorithm helps the robot determine the best time for a given leg to switch from swinging in the air to stepping on the ground. For example, if the robot steps on a light twig versus a hard, heavy rock, how it reacts -- and whether it continues to carry through with a step, or pulls back and swings its leg instead -- can make or break its balance. "When it comes to switching from the air to the ground, the switching has to be very well-done, " Kim says. "This algorithm is really about, 'When is a safe time to commit my footstep?'" The contact detection algorithm helps the robot determine the best time to transition a leg between swing and step, by constantly calculating for each leg three probabilities: the probability of a leg making contact with the ground, the probability of the force generated once the leg hits the ground, and the probability that the leg will be in midswing. The algorithm calculates these probabilities based on data from gyroscopes, accelerometers, and joint positions of the legs, which record the leg's angle and height with respect to the ground. If, for example, the robot unexpectedly steps on a wooden block, its body will suddenly tilt, shifting the angle and height of the robot. That data will immediately feed into calculating the three probabilities for each leg, which the algorithm will combine to estimate whether each leg should commit to pushing down on the ground, or lift up and swing away in order to keep its balance -- all while the robot is virtually blind. "If humans close our eyes and make a step, we have a mental model for where the ground might be, and can prepare for it. But we also rely on the feel of touch of the ground, " Kim says. "We are sort of doing the same thing by combining multiple [sources of] information to determine the transition time." The researchers tested the algorithm in experiments with the Cheetah 3 trotting on a laboratory treadmill and climbing on a staircase. Both surfaces were littered with random objects such as wooden blocks and rolls of tape. "It doesn't know the height of each step and doesn't know there are obstacles on the stairs, but it just ploughs through without losing its balance, " Kim says. "Without that algorithm, the robot was very unstable and fell easily." Future force The robot's blind locomotion was also partly due to the model-predictive control algorithm, which predicts how much force a given leg should apply once it has committed to a step. "The contact detection algorithm will tell you, 'this is the time to apply forces on the ground, '" Kim says. "But once you're on the ground, now you need to calculate what kind of forces to apply so you can move the body in the right way." The model-predictive control algorithm calculates the multiplicative positions of the robot's body and legs a half-second into the future if a certain force is applied by any given leg as it makes contact with the ground. "Say someone kicks the robot sideways, " Kim says. "When the foot is already on the ground, the algorithm decides, 'How should I specify the forces on the foot? Because I have an undesirable velocity on the left, so I want to apply a force in the opposite direction to kill that velocity. If I apply 100 newtons’s in this opposite direction, what will happen a half second later?" The algorithm is designed to make these calculations for each leg every 50 milliseconds, or 20 times per second. In experiments, researchers introduced unexpected forces by kicking and shoving the robot as it trotted on a treadmill, and yanking it by the leash as it climbed up an obstacle-laden staircase. They found that the model-predictive algorithm enabled the robot to quickly produce counter-forces to regain its balance and keep moving forward, without tipping too far in the opposite direction. "It's thanks to that predictive control that can apply the right forces on the ground, combined with this contact transition algorithm that makes each contact very quick and secure, " Kim says. The team had already added cameras to the robot to give it visual feedback of its surroundings. This will help in mapping the general environment and will give the robot a visual heads-up on larger obstacles such as doors and walls. But for now, the team is working to further improve the robot's blind locomotion "We want a very good controller without vision first, " Kim says. "And when we do add vision, even if it might give you the wrong information, the leg should be able to handle (obstacles). Because what if it steps on something that a camera can't see? What will it do? That's where blind locomotion can help. We don't want to trust our vision too much." This research was supported, in part, by Naver, Toyota Research Institute, Foxconn, and Air Force Office of Scientific Research. 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Next-generation robotic cockroach can explore under water environments. The next generation of Harvard's Ambulatory Micro robot (HAMR) can walk on land, swim on the surface of water, and walk underwater, opening up new environments for this little bot to explore. In nature, cockroaches can survive underwater for up to 30 minutes. Now, a robotic cockroach can do even better. Harvard's Ambulatory Microrobot, known as HAMR, can walk on land, swim on the surface of water, and walk underwater for as long as necessary, opening up new environments for this little bot to explore. This next generation HAMR uses multifunctional foot pads that rely on surface tension and surface tension induced buoyancy when HAMR needs to swim but can also apply a voltage to break the water surface when HAMR needs to sink. This process is called electro wetting, which is the reduction of the contact angle between a material and the water surface under an applied voltage. This change of contact angle makes it easier for objects to break the water surface. Moving on the surface of water allows a microrobot to evade submerged obstacles and reduces drag. Using four pairs of asymmetric flaps and custom designed swimming gaits, HAMR robo-paddles on the water surface to swim. Exploiting the unsteady interaction between the robot's passive flaps and the surrounding water, the robot generates swimming gaits similar to that of a diving beetle. This allows the robot to effectively swim forward and turn. "This research demonstrates that microrobotics can leverage small-scale physics—in this case surface tension—to perform functions and capabilities that are challenging for larger robots, " said Kevin Chen, a postdoctoral fellow at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and first author of the paper. The most recent research is published in the journal Nature Communications. "HAMR's size is key to its performance, " said Neel Doshi, graduate student at SEAS and co-author of the paper. "If it were much bigger, it would be challenging to support the robot with surface tension and if it were much smaller, the robot might not be able to generate enough force to break it." HAMR weighs 1.65 grams (about as much as a large paper clip), can carry 1.44 grams of additional payload without sinking and can paddle its legs with a frequency up to 10 Hz. It's coated in Parylene to keep it from shorting under water. Once below the surface of the water, HAMR uses the same gait to walk as it does on dry land and is just as mobile. To return to dry land HAMR faces enormous challenge from the water's hold. A water surface tension force that is twice the robot weight pushes down on the robot, and in addition the induced torque causes a dramatic increase of friction on the robot's hind legs. The researchers stiffened the robot's transmission and installed soft pads to the robot's front legs to increase payload capacity and redistribute friction during climbing. Finally, walking up a modest incline, the robot is able break out of the water's hold. This robot nicely illustrates some of the challenges and opportunities with small-scale robots, " said senior author Robert Wood, Charles River Professor of Engineering and Applied Sciences at SEAS and core faculty member of the Harvard Wyss Institute for Biologically Inspired Engineering. "Shrinking brings opportunities for increased mobility—such as walking on the surface of water—but also challenges since the forces that we take for granted at larger scales can start to dominate at the size of an insect." 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.
Using your body to control a drone is more effective than a joystick. If you've ever been chastised for throwing your entire body around during gaming (because physically leaning into track corners definitely helps somehow), here's a bit of science-backed vindication. Researchers in Switzerland have discovered that using your torso to control a drone is far more effective than using a joystick. The team from EPFL monitored the body movements and muscular activity of 17 people, each with 19 markers placed all over their upper bodies. The participants then followed the actions of a virtual drone through simulated landscapes, via virtual reality goggles. By observing motion patterns, the scientists found that only four markers located on the torso were needed to pilot a drone through an obstacle course and that the method outperformed joystick control in both precision and reliability. The study's lead author, Jenifer Miehlbradt of EPFL's Translational Neuroengineering Laboratory, said: "Using your torso really gives you the feeling that you are actually flying. Joysticks, on the other hand, are of simple design but mastering their use to precisely control distant objects can be challenging." The proof-of-concept system still depends on body markers and external motion detectors to work, so the team's next challenge will be making the tech wearable and completely independent. However, the range of applications for it is enormous. Being able to virtually fly while your head, limbs, hand and feet are free to perform other tasks could be a major development for gaming, drone control or even the planes of the future. 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,
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