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IEEE Spectrum
  • 18 Sep 2019

    The idea behind swarm robots is to replace discrete, expensive, breakable uni-tasking components with a whole bunch of much simpler, cheaper, and replaceable robots that can work together to do the same sorts of tasks. Unfortunately, all of those swarm robots end up needing their own computing and communications and stuff if you want to get them to do what you want them to do. 

    A different approach to swarm robotics is to use a swarm of much cheaper robots that are far less intelligent. In fact, they may not have to be intelligent at all, if you can rely on their physical characteristics to drive them instead. These swarms are “stochastic,” meaning that their motions are randomly determined, but if you’re clever and careful, you can still get them to do specific things.   

    Georgia Tech has developed some little swarm robots called “smarticles” that can’t really do much at all on their own, but once you put them together into a jumble, their randomness can actually accomplish something.

    Honestly, calling these particle robots “smart” might be giving them a bit too much credit, because they’re actually kind of dumb and strictly speaking not capable of all that much on their own. A single smarticle weighs 35 grams, and consists of some little 3D-printed flappy bits attached to servos, plus an Arduino Pro Mini, a battery, and a light or sound sensor. When its little flappy bits are activated, each smarticle can move slightly, but a single one mostly just moves around in a square and then will gradually drift in a mostly random direction over time.

    It gets more interesting when you throw a whole bunch of smarticles into a constrained area. A small collection of five or 10 smarticles constrained together form a “supersmarticle,” but besides being in close proximity to one another, the smarticles within the supersmarticle aren’t communicating or anything like that. As far as each smarticle is concerned, they’re independent, but weirdly, a bumble of them can work together without working together.

    “These are very rudimentary robots whose behavior is dominated by mechanics and the laws of physics,” said Dan Goldman, a Dunn Family Professor in the School of Physics at the Georgia Institute of Technology.

    The researchers noticed that if one small robot stopped moving, perhaps because its battery died, the group of smarticles would begin moving in the direction of that stalled robot. Graduate student Ross Warkentin learned he could control the movement by adding photo sensors to the robots that halt the arm flapping when a strong beam of light hits one of them.

    “If you angle the flashlight just right, you can highlight the robot you want to be inactive, and that causes the ring to lurch toward or away from it, even though no robots are programmed to move toward the light,” Goldman said. “That allowed steering of the ensemble in a very rudimentary, stochastic way.”

    It turns out that it’s possible to model this behavior, and control a supersmarticle with enough fidelity to steer it through a maze. And while these particular smarticles aren’t all that small, strictly speaking, the idea is to develop techniques that will work when robots are scaled way way down to the point where you can't physically fit useful computing in there at all. 

    The researchers are also working on some other concepts, like these:

    Er, yeah. I’m…not sure I really want there to be a bipedal humanoid robot built out of a bunch of tiny robots. Like, that seems creepy somehow, you know? I’m totally okay with slugs, but let’s not get crazy. 

    “A robot made of robots: Emergent transport and control of a smarticle ensemble, by William Savoie, Thomas A. Berrueta, Zachary Jackson, Ana Pervan, Ross Warkentin, Shengkai Li, Todd D. Murphey, Kurt Wiesenfeld, and Daniel I. Goldman” from the Georgia Institute of Technology, appears in the current issue of Science Robotics.

  • 17 Sep 2019

    We humans spend most of our time getting hungry or eating, which must be really inconvenient for the people who have to produce food for everyone. For a sustainable and tasty future, we’ll need to make the most of what we’ve got by growing more food with less effort, and that’s where the robots can help us out a little bit.

    FarmWise, a California-based startup, is looking to enhance farming efficiency by automating everything from seeding to harvesting, starting with the worst task of all: weeding. And they’ve just raised US $14.5 million to do it.

    FarmWise’s autonomous, AI-enabled robots are designed to solve farmers’ most pressing challenges by performing a variety of farming functions - starting with weeding, and providing personalized care to every plant they touch. Using machine learning models, computer vision and high-precision mechanical tools, FarmWise’s sophisticated robots cleanly pick weeds from fields, leaving crops with the best opportunity to thrive while eliminating harmful chemical inputs. To date, FarmWise’s robots have efficiently removed weeds from more than 10 million plants.

    FarmWise is not the first company to work on large mobile farming robots. A few years ago, we wrote about DeepField Robotics and their giant weed-punching robot. But considering how many humans there are, and how often we tend to get hungry, it certainly seems like there’s plenty of opportunity to go around.

    Weeding is just one thing that farm robots are able to do. FarmWise is collecting massive amounts of data about every single plant in an entire field, practically on the per-leaf level, which is something that hasn’t been possible before. Data like this could be used for all sorts of things, but generally, the long-term hope is that robots could tend to every single plant individually—weeding them, fertilizing them, telling them what good plants they are, and then mercilessly yanking them out of the ground at absolute peak ripeness. It’s not realistic to do this with human labor, but it’s the sort of data-intensive and monotonous task that robots could be ideal for. 

    The question with robots like this is not necessarily whether they can do the job that they were created for, because generally, they can—farms are structured enough environments that they lend themselves to autonomous robots, and the tasks are relatively well defined. The issue right now, I think, is whether robots are really time- and cost-effective for farmers. Capable robots are an expensive investment, and even if there is a shortage of human labor, will robots perform well enough to convince farmers to adopt the technology? That’s a solid maybe, and here’s hoping that FarmWise can figure out how to make it work.

    [ FarmWise ]

  • 17 Sep 2019

    After 25 million games, the AI agents playing hide-and-seek with each other had mastered four basic game strategies. The researchers expected that part.

    After a total of 380 million games, the AI players developed strategies that the researchers didn’t know were possible in the game environment—which the researchers had themselves created. That was the part that surprised the team at OpenAI, a research company based in San Francisco.

    The AI players learned everything via a machine learning technique known as reinforcement learning. In this learning method, AI agents start out by taking random actions. Sometimes those random actions produce desired results, which earn them rewards. Via trial-and-error on a massive scale, they can learn sophisticated strategies.

    In the context of games, this process can be abetted by having the AI play against another version of itself, ensuring that the opponents will be evenly matched. It also locks the AI into a process of one-upmanship, where any new strategy that emerges forces the opponent to search for a countermeasure. Over time, this “self-play” amounted to what the researchers call an “auto-curriculum.” 

    According to OpenAI researcher Igor Mordatch, this experiment shows that self-play “is enough for the agents to learn surprising behaviors on their own—it’s like children playing with each other.”

    Reinforcement is a hot field of AI research right now. OpenAI’s researchers used the technique when they trained a team of bots to play the video game Dota 2, which squashed a world-champion human team last April. The Alphabet subsidiary DeepMind has used it to triumph in the ancient board game Go and the video game StarCraft

    Aniruddha Kembhavi, a researcher at the Allen Institute for Artificial Intelligence (AI2) in Seattle, says games such as hide-and-seek offer a good way for AI agents to learn “foundational skills.” He worked on a team that taught their AllenAI to play Pictionary with humans, viewing the gameplay as a way for the AI to work on common sense reasoning and communication. “We are, however, quite far away from being able to translate these preliminary findings in highly simplified environments into the real world,” says Kembhavi. 

    In OpenAI’s game of hide-and-seek, both the hiders and the seekers received a reward only if they won the game, leaving the AI players to develop their own strategies. Within a simple 3D environment containing walls, blocks, and ramps, the players first learned to run around and chase each other (strategy 1). The hiders next learned to move the blocks around to build forts (2), and then the seekers learned to move the ramps (3), enabling them to jump inside the forts. Then the hiders learned to move all the ramps into their forts before the seekers could use them (4). 

    The two strategies that surprised the researchers came next. First the seekers learned that they could jump onto a box and “surf” it over to a fort (5), allowing them to jump in—a maneuver that the researchers hadn’t realized was physically possible in the game environment. So as a final countermeasure, the hiders learned to lock all the boxes into place (6) so they weren’t available for use as surfboards. 

    In this circumstance, having AI agents behave in an unexpected way wasn’t a problem: They found different paths to their rewards, but didn’t cause any trouble. However, you can imagine situations in which the outcome would be rather serious. Robots acting in the real world could do real damage. And then there’s Nick Bostrom’s famous example of a paper clip factory run by an AI, whose goal is to make as many paper clips as possible. As Bostrom told IEEE Spectrum back in 2014, the AI might realize that “human bodies consist of atoms, and those atoms could be used to make some very nice paper clips.”

    Bowen Baker, another member of the OpenAI research team, notes that it’s hard to predict all the ways an AI agent will act inside an environment—even a simple one. “Building these environments is hard,” he says. “The agents will come up with these unexpected behaviors, which will be a safety problem down the road when you put them in more complex environments.” 

    AI researcher Katja Hofmann at Microsoft Research Cambridge, in England, has seen a lot of gameplay by AI agents: She started a competition that uses Minecraft as the playing field. She says the emergent behavior seen in this game, and in prior experiments by other researchers, shows that games can be a useful for studies of safe and responsible AI.

    “I find demonstrations like this, in games and game-like settings, a great way to explore the capabilities and limitations of existing approaches in a safe environment,” says Hofmann. “Results like these will help us develop a better understanding on how to validate and debug reinforcement learning systems–a crucial step on the path towards real-world applications.”

    Baker says there’s also a hopeful takeaway from the surprises in the hide-and-seek experiment. “If you put these agents into a rich enough environment they will find strategies that we never knew were possible,” he says. “Maybe they can solve problems that we can’t imagine solutions to.”

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