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If it feels like we’re in the midst of robot renaissance right now, perhaps it’s because we are. There is a new crop of robots under development that we’ll soon be able to buy and install in our factories or interact with in our homes. And while they might look like robots past on the outside, their brains are actually much different.
Today’s robots aren’t rigid automatons built by a manufacturer solely to perform a single task faster than, cheaper than and, ideally, without much input from humans. Rather, today’s robots can be remarkably adaptable machines that not only learn from their experiences, but can even be designed to work hand in hand with human colleagues. Commercially available (or soon to be) technologies such as Jibo, Baxter and Amazon Echo are three well-known examples of what’s now possible, but they’re also just the beginning.
Different technological advances have spurred the development of smarter robots depending on where you look, although they all boil down to training. “It’s not that difficult to builtd the body of the robot,” said Eugene Izhikevich, founder and CEO of robotics startup Brain Corporation, “but the reason we don’t have that many robots in our homes taking care of us is it’s very difficult to program the robots.”
Essentially, we want robots that can perform more than one function, or perform one function very well. And it’s difficult to program a robot to do multiple things, or at least the things that users might want, and it’s especially difficult to program to do these things in different settings. My house is different than your house, my factory is different than your factory.
“The ability to handle variations is what enables these robots to go out into the world and actually be useful,” said Ashutosh Saxena, a Stanford University visiting professor and head of the RoboBrain project. (Saxena will be presenting on this topic at Gigaom’s Structure Data conference March 18 and 19 in New York, along with Julie Shah of MIT’s Interactive Robotics Group. Our Structure Intelligence conference, which focuses on the cutting edge in artificial intelligence, takes place in September in San Francisco.)
That’s where training comes into play. In some cases, particularly projects residing within universities and research centers, the internet has arguably been a driving force behind advances in creating robots that learn. That’s the case with RoboBrain, a collaboration among Stanford, Cornell and a few other universities that crawls the web with the goal of building a web-accessible knowledge graph for robots. RoboBrain’s researchers aren’t building robots, but rather a database of sorts (technically, more of a representation of concepts — what an egg looks like, how to make coffee or how to speak to humans, for example) that contains information robots might need in order to function within a home, factory or elsewhere.
RoboBrain encompasses a handful of different projects addressing different contexts and different types of knowledge, and the web provides an endless store of pictures, YouTube videos and other content that can teach RoboBrain what’s what and what’s possible. The “brain” is trained with examples of things it should recognize and tasks it should understand, as well as with reinforcement in the form of thumbs up and down when it posits a fact it has learned.
For example, one of its flagship projects, which Saxena started at Cornell, is called Tell Me Dave. In that project, researchers and crowdsourced helpers across the web train a robot to perform certain tasks by walking it through the necessary steps for tasks such as cooking ramen noodles. In order for it to complete a task, the robot needs to know quite a bit: what each object it sees in the kitchen is, what functions it performs, how it operates and at which step it’s used in any given process. In the real world, it would need to be able to surface this knowledge upon, presumably, a user request spoken in natural language — “Make me ramen noodles.”
Multiply that by any number of tasks someone might actually want a robot to perform, and it’s easy to see why RoboBrain exists. Tell Me Dave can only learn so much, but because it’s accessing that collective knowledge base or “brain,” it should theoretically know things it hasn’t specifically trained on. Maybe how to paint a wall, for example, or that it should give human beings in the same room at least 18 inches of clearance.
There are now plenty of other examples of robots learning by example, often in lab environments or, in the case of some recent DARPA research using the aforementioned Baxter robot, watching YouTube videos about cooking (pictured above).
Advances in deep learning — the artificial intelligence technique du jour for machine-perception tasks such as computer vision, speech recognition and language understanding — also stand to expedite the training of robots. Deep learning algorithms trained on publicly available images, video and other media content can help robots recognize the objects they’re seeing or the words they’re hearing; Saxena said RoboBrain uses deep learning to train robots on proper techniques for moving and grasping objects.
However, there’s a different school of thought that says robots needn’t necessarily be as smart as RoboBrain wants to make them, so long as they can at least be trained to know right from wrong. That’s what Izhikevich and his aforementioned startup, Brain Corporation, are out to prove. It has built a specialized hardware and software platform, based on the idea of spiking neurons, that Izhikevich says can go inside any robot and “you can train your robot on different behaviors like you can train an animal.”
That is to say, for example, that a vacuum robot powered by the company’s operating system (called BrainOS) won’t be able to recognize that a cat is a cat, but it will be able to learn from its training that that object — whatever it is — is something to avoid while vacuuming. Conceivably, as long as they’re trained well enough on what’s normal in a given situation or what’s off limits, BrainOS-powered robots could be trained to follow certain objects or detect new objects or do lots of other things.
If there’s one big challenge to the notion of training robots versus just programming them, it’s that consumers or companies that use the robots will probably have to do a little work themselves. Izhikevich noted that the easiest model might be for BrainOS robots to be trained in the lab, and then have that knowledge turned into code that’s preinstalled in commercial versions. But if users want to personalize robots for their specific environments or uses, they’re probably going to have to train it.
As the internet of things and smart devices, in general, catch on, consumers are already getting used the idea — sometimes begrudgingly. Even when it’s something as simple as pressing a few buttons in an app, like training a Nest thermostat or a Canary security camera, training our devices can get tiresome. Even those of us who understand how the algorithms work can get get annoyed.
“For most applications, I don’t think consumers want to do anything,” Izhikevich said. “You want to press the ‘on’ button and the robot does everything autonomously.”
But maybe three years from now, by which time Izhikevich predicts robots powered by Brain Corporation’s platform will be commercially available, consumers will have accepted one inherent tradeoff in this new era of artificial intelligence — that smart machines are, to use Izhikevich’s comparison, kind of like animals. Specifically, dogs: They can all bark and lick, but turning them into seeing eye dogs or K-9 cops, much less Lassie, is going to take a little work.