Collaborative learning — for robots: New algorithm
Date: June 25, 2014
Source: Massachusetts Institute of Technology
Summary: Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It’s also the technique that autonomous robots typically use to build models of their environments. A new algorithm lets independent agents collectively produce a machine-learning model without aggregating data.
Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It’s also the technique that autonomous robots typically use to build models of their environments.
That type of model-building gets complicated, however, in cases in which clusters of robots work as teams. The robots may have gathered information that, collectively, would produce a good model but which, individually, is almost useless. If constraints on power, communication, or computation mean that the robots can’t pool their data at one location, how can they collectively build a model?
At the Uncertainty in Artificial Intelligence conference in July, researchers from MIT’s Laboratory for Information and Decision Systems will answer that question. They present an algorithm in which distributed agents — such as robots exploring a building — collect data and analyze it independently. Pairs of agents, such as robots passing each other in the hall, then exchange analyses.
In experiments involving several different data sets, the researchers’ distributed algorithm actually outperformed a standard algorithm that works on data aggregated at a single location.
“A single computer has a very difficult optimization problem to solve in order to learn a model from a single giant batch of data, and it can get stuck at bad solutions,” says Trevor Campbell, a graduate student in aeronautics and astronautics at MIT, who wrote the new paper with his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics. “If smaller chunks of data are first processed by individual robots and then combined, the final model is less likely to get stuck at a bad solution.”
Campbell says that the work was motivated by questions about robot collaboration. But it could also have implications for big data, since it would allow distributed servers to combine the results of their data analyses without aggregating the data at a central location.
“This procedure is completely robust to pretty much any network you can think of,” Campbell says. “It’s very much a flexible learning algorithm for decentralized networks.”
To get a sense of the problem Campbell and How solved, imagine a team of robots exploring an unfamiliar office building. If their learning algorithm is general enough, they won’t have any prior notion of what a chair is, or a table, let alone a conference room or an office. But they could determine, for instance, that some rooms contain a small number of chair-shaped objects together with roughly the same number of table-shaped objects, while other rooms contain a large number of chair-shaped objects together with a single table-shaped object.
Over time, each robot will build up its own catalogue of types of rooms and their contents. But inaccuracies are likely to creep in: One robot, for instance, might happen to encounter a conference room in which some traveler has left a suitcase and conclude that suitcases are regular features of conference rooms. Another might enter a kitchen while the coffeemaker is obscured by the open refrigerator door and leave coffeemakers off its inventory of kitchen items.
Ideally, when two robots encountered each other, they would compare their catalogues, reinforcing mutual observations and correcting omissions or overgeneralizations. The problem is that they don’t know how to match categories. Neither knows the label “kitchen” or “conference room”; they just have labels like “room 1” and “room 3,” each associated with different lists of distinguishing features. But one robot’s room 1 could be another robot’s room 3.
With Campbell and How’s algorithm, the robots try to match categories on the basis of shared list items. This is bound to lead to errors: One robot, for instance, may have inferred that sinks and pedal-operated trashcans are distinguishing features of bathrooms, another that they’re distinguishing features of kitchens. But they do their best, combining the lists that they think correspond.
When either of those robots meets another robot, it performs the same procedure, matching lists as best it can. But here’s the crucial step: It then pulls out each of the source lists independently and rematches it to the others, repeating this process until no reordering results. It does this again with every new robot it encounters, gradually building more and more accurate models.
This relatively straightforward procedure results from some pretty sophisticated mathematical analysis, which the researchers present in their paper. “The way that computer systems learn these complex models these days is that you postulate a simpler model and then use it to approximate what you would get if you were able to deal with all the crazy nuances and complexities,” Campbell says. “What our algorithm does is sort of artificially reintroduce structure, after you’ve solved that easier problem, and then use that artificial structure to combine the models properly.”
In a real application, the robots probably wouldn’t just be classifying rooms according to the objects they contain: They’d also be classifying the objects themselves, and probably their uses. But Campbell and How’s procedure generalizes to other learning problems just as well.
The example of classifying rooms according to content, moreover, is similar in structure to a classic problem in natural language processing called topic modeling, in which a computer attempts to use the relative frequency of words to classify documents according to topic. It would be wildly impractical to store all the documents on the Web in a single location, so that a traditional machine-learning algorithm could provide a consistent classification scheme for all of them. But Campbell and How’s algorithm means that scattered servers could churn away on the documents in their own corners of the Web and still produce a collective topic model.
“Distributed computing will play a critical role in the deployment of multiple autonomous agents, such as multiple autonomous land and airborne vehicles,” says Lawrence Carin, a professor of electrical and computer engineering and vice provost for research at Duke University. “The distributed variational method proposed in this paper is computationally efficient and practical. One of the keys to it is a technique for handling the breaking of symmetries manifested in Bayesian inference. The solution to this problem is very novel and is likely to be leveraged in the future by other researchers.”
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Robot can be programmed by casually talking to it
Date: June 23, 2014
Source: Cornell University
Summary: A professor of computer science is teaching robots to understand instructions in natural language from various speakers, account for missing information, and adapt to the environment at hand.
Robots are getting smarter, but they still need step-by-step instructions for tasks they haven’t performed before. Before you can tell your household robot “Make me a bowl of ramen noodles,” you’ll have to teach it how to do that. Since we’re not all computer programmers, we’d prefer to give those instructions in English, just as we’d lay out a task for a child.
But human language can be ambiguous, and some instructors forget to mention important details. Suppose you told your household robot how to prepare ramen noodles, but forgot to mention heating the water or tell it where the stove is.
In his Robot Learning Lab, Ashutosh Saxena, assistant professor of computer science at Cornell University, is teaching robots to understand instructions in natural language from various speakers, account for missing information, and adapt to the environment at hand.
Saxena and graduate students Dipendra K. Misra and Jaeyong Sung will describe their methods at the Robotics: Science and Systems conference at the University of California, Berkeley, July 12-16.
The robot may have a built-in programming language with commands like find (pan); grasp (pan); carry (pan, water tap); fill (pan, water); carry (pan, stove) and so on. Saxena’s software translates human sentences, such as “Fill a pan with water, put it on the stove, heat the water. When it’s boiling, add the noodles.” into robot language. Notice that you didn’t say, “Turn on the stove.” The robot has to be smart enough to fill in that missing step.
Saxena’s robot, equipped with a 3-D camera, scans its environment and identifies the objects in it, using computer vision software previously developed in Saxena’s lab. The robot has been trained to associate objects with their capabilities: A pan can be poured into or poured from; stoves can have other objects set on them, and can heat things. So the robot can identify the pan, locate the water faucet and stove and incorporate that information into its procedure. If you tell it to “heat water” it can use the stove or the microwave, depending on which is available. And it can carry out the same actions tomorrow if you’ve moved the pan, or even moved the robot to a different kitchen.
Other workers have attacked these problems by giving a robot a set of templates for common actions and chewing up sentences one word at a time. Saxena’s research group uses techniques computer scientists call “machine learning” to train the robot’s computer brain to associate entire commands with flexibly defined actions. The computer is fed animated video simulations of the action — created by humans in a process similar to playing a video game — accompanied by recorded voice commands from several different speakers.
The computer stores the combination of many similar commands as a flexible pattern that can match many variations, so when it hears “Take the pot to the stove,” “Carry the pot to the stove,” “Put the pot on the stove,” “Go to the stove and heat the pot” and so on, it calculates the probability of a match with what it has heard before, and if the probability is high enough, it declares a match. A similarly fuzzy version of the video simulation supplies a plan for the action: Wherever the sink and the stove are, the path can be matched to the recorded action of carrying the pot of water from one to the other.
Of course the robot still doesn’t get it right all the time. To test, the researchers gave instructions for preparing ramen noodles and for making affogato — an Italian dessert combining coffee and ice cream: “Take some coffee in a cup. Add ice cream of your choice. Finally, add raspberry syrup to the mixture.”
The robot performed correctly up to 64 percent of the time even when the commands were varied or the environment was changed, and it was able to fill in missing steps. That was three to four times better than previous methods, the researchers reported, but “There is still room for improvement.”
You can teach a simulated robot to perform a kitchen task at the “Tell me Dave” website, and your input there will become part of a crowdsourced library of instructions for the Cornell robots. Aditya Jami, visiting researcher at Cornell, is helping Tell Me Dave to scale the library to millions of examples. “With crowdsourcing at such a scale, robots will learn at a much faster rate,” Saxena said.
Further information: http://tellmedave.cs.cornell.edu/