But that hesitation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even\u00a0leap among us. The machines have arrived.<\/p>\n
You may be worried a robot is going to steal your job, and we get that. This is capitalism, after all, and automation is inevitable. But you may be more likely to work\u00a0alongside<\/em>\u00a0a robot in the near future than have one replace you. And even better news: You\u2019re more likely to make friends with a robot than have one murder you. Hooray for the future!<\/p>\n The History of Robots<\/strong><\/p>\n The definition of \u201crobot\u201d has been confusing from the very beginning. The word first appeared in 1921, in Karel Capek\u2019s play\u00a0R.U.R.<\/em>, or Rossum’s Universal Robots. \u201cRobot\u201d comes from the Czech for \u201cforced labor.\u201d These robots were robots more in spirit than form, though. They looked like humans, and instead of being made of metal, they were made of chemical batter. The robots were far more efficient than their human counterparts, and also way more murder-y\u2014they ended up\u00a0going on a killing spree.<\/p>\n R.U.R.<\/em>\u00a0would establish the trope of the Not-to-Be-Trusted Machine (e.g.,\u00a0Terminator<\/em>,\u00a0The Stepford Wives<\/em>,\u00a0Blade Runner<\/em>, etc.) that continues to this day\u2014which is not to say pop culture hasn\u2019t embraced friendlier robots. Think Rosie from\u00a0The Jetsons<\/em>. (Ornery, sure, but certainly not homicidal.) And it doesn\u2019t get much family-friendlier than Robin Williams\u00a0asBicentennial Man<\/em>.<\/p>\n The real-world definition of \u201crobot\u201d is just as slippery as those fictional depictions. Ask 10 roboticists and you\u2019ll get 10 answers. But they do agree on some\u00a0general guidelines: A robot is an intelligent, physically embodied machine. A robot can perform tasks autonomously. And a robot can sense and manipulate its environment.<\/p>\n Think of a simple drone that you pilot around. That\u2019s no robot. But give a drone the power to take off and land on its own and sense objects and suddenly it\u2019s a lot more robot-ish. It\u2019s the intelligence and sensing and autonomy that\u2019s key.<\/p>\n But it wasn\u2019t until the 1960s that a company built something that started meeting those guidelines. That\u2019s when SRI International in Silicon Valley developed\u00a0Shakey, the first truly mobile and perceptive robot. This tower on wheels was well-named\u2014awkward, slow, twitchy. Equipped with a camera and bump sensors, Shakey could navigate a complex environment. It wasn\u2019t a particularly confident-looking machine, but it was the beginning of the robotic revolution.<\/p>\n Around the time Shakey was trembling about, robot arms were beginning to transform manufacturing. The first among them was\u00a0Unimate, which welded auto bodies. Today, its descendants rule car factories, performing tedious, dangerous tasks with far more precision and speed than any human could muster. Even though they\u2019re stuck in place, they still very much fit our definition of a robot\u2014they\u2019re intelligent machines that sense and manipulate their environment.<\/p>\n Robots, though, remained largely confined to factories and labs, where they either rolled about or were stuck in place lifting objects. Then, in the mid-1980s Honda started up a humanoid robotics program. It developed P3, which could walk pretty darn good and also wave and shake hands, much to the delight of a\u00a0roomful of suits. The work would culminate in Asimo, the famed biped, which once tried to\u00a0take out President Obama\u00a0with a well-kicked soccer ball. (OK, perhaps it was more innocent than that.)<\/p>\n Today, advanced robots are popping up\u00a0everywhere<\/em>. For that you can thank three technologies in particular: sensors, actuators, and AI.<\/p>\n So, sensors. Machines that roll on sidewalks to\u00a0deliver falafelcan only navigate our world thanks in large part to the 2004 Darpa Grand Challenge, in which teams of roboticists cobbled together\u00a0self-driving cars\u00a0to race through the desert. Their secret? Lidar, which spews lasers to build a 3-D map of the world. The ensuing private-sector race to develop self-driving cars has dramatically driven down the price of lidar, to the point that engineers can create perceptive robots on the (relative) cheap.<\/p>\n Lidar is often combined with something called machine vision\u20142-D or 3-D cameras that allow the robot to build an even better picture of its world. You know how Facebook automatically recognizes your mug and tags you in pictures? Same principle with robots. Fancy algorithms allow them to\u00a0pick out certain landmarks or objects.<\/p>\n Sensors are what keep robots from running us down. They\u2019re why a robot mule of sorts can keep an eye on you, following you and\u00a0schlepping your stuff around; machine vision also allows robots to\u00a0scan cherry trees to determine where best to shake them\u00a0, helping fill massive labor gaps in agriculture.<\/p>\n Within each of these robots is the next secret ingredient: the\u00a0actuator, which is a fancy word for the combo electric motor and gearbox that you\u2019ll find in a robot\u2019s joint. It\u2019s this actuator that determines how strong a robot is and how smoothly or\u00a0not smoothly it moves. Without actuators, robots would crumple like rag dolls. Even relatively simple robots like Roombas owe their existence to actuators. Self-driving cars, too, are loaded with the things.<\/p>\n Actuators are great for powering massive robot arms on a car assembly line, but a newish field, known as soft robotics, is devoted to creating actuators that operate on a whole new level. Unlike mule robots, soft robots are generally squishy, and use air or oil to get themselves moving. So for instance, one particular kind of robot muscle uses electrodes to squeeze a pouch of oil, expanding and contracting to\u00a0tug on weights. Unlike with bulky traditional actuators, you could stack a bunch of these to magnify the strength: A robot named Kengoro, for instance, moves with 116 actuators that tug on cables, allowing the machine to do\u00a0unsettlingly human maneuvers like pushups. It\u2019s a far more natural-looking form of movement than what you\u2019d get with traditional electric motors housed in the joints.<\/p>\n And then there\u2019s Boston Dynamics, which created the Atlas humanoid robot for the Darpa Robotics Challenge in 2013. At first, university robotics research teams struggled to get the machine to tackle the basic tasks of the original 2013 challenge and the finals round in 2015, like turning valves and opening doors. But Boston Dynamics has since that time turned Atlas into a marvel that can\u00a0do backflips, far outpacing other bipeds that still have a hard time walking. (Unlike the Terminator, though, it does not pack heat.) Boston Dynamics is also working on a quadruped robot called SpotMini, which can recover in unsettling fashion when humans kick or\u00a0tug on it. That kind of stability will be key if we want to build a world where we don\u2019t spend all our time helping robots out of jams. And it\u2019s all thanks to the humble actuator.<\/p>\n At the same time that robots like Atlas and SpotMini are getting more physically robust, they\u2019re getting smarter, thanks to AI. Robotics seems to be reaching an inflection point, where processing power and artificial intelligence are combining to\u00a0truly ensmarten the machines. And for the machines, just as in humans, the senses and intelligence are inseparable\u2014if you pick up a fake apple and don\u2019t realize it\u2019s plastic before shoving it in your mouth, you\u2019re not very smart. This is a fascinating frontier in robotics (replicating the sense of touch, not eating fake apples). A company called SynTouch, for instance, has developed robotic fingertips that can\u00a0pick up a range of sensations, from temperature to coarseness.<\/p>\n As sensors are getting cheaper, the superpowered processors required for AI are doing the same. Thanks to advances in gaming and VR\u2014graphics processing units, or GPUs, are helping mobile robots to perform complex computations right onboard the machine, as opposed to in the cloud, which means they can still operate if they lose their connection. This is particularly important for powering that machine vision, which allows a robot like Kuri to\u00a0recognize your face. To help you, by the way, not hunt you or anything.<\/p>\n Ideally, that is.<\/p>\n The Future of Robots<\/strong><\/p>\n Increasingly sophisticated machines may populate our world, but for robots to be really useful, they\u2019ll have to become more self-sufficient. After all, it would be impossible to program a home robot with the instructions for gripping each and every object it ever might encounter. You want it to learn on its own, which is where advances in artificial intelligence come in.<\/p>\n Take Brett the robot. In a UC Berkeley lab, the humanoid has taught itself to conquer one of those children\u2019s puzzles where you cram pegs into different shaped holes. It did so by trial and error through a process called reinforcement learning. No one told it\u00a0how<\/em>\u00a0to get a square peg into a square hole, just that it\u00a0needed<\/em>\u00a0to. So by making random movements and getting a digital reward (basically,\u00a0yes, do that kind of thing again<\/em>) each time it got closer to success, Brett\u00a0learned something new on its own. The process is super slow, sure, but with time roboticists will hone the machines\u2019 ability to teach themselves novel skills in novel environments, which is pivotal if we don\u2019t want to get stuck babysitting them.<\/p>\n![\"\"](\"https:\/\/media.wired.com\/photos\/5a726e8b8d395a4a142281a5\/master\/w_650,c_limit\/howwegotthere.jpg\")
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