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Teaching Commercial Robots How to Adapt

Many commercial robotic arms perform "pick and place" tasks - the arm picks up an object in one location from an assembly line and places it in another. Usually, the objects are positioned so that the arm can easily grasp them and the appendage that does the grasping may even be tailored to the objects' shape. General-purpose household robots, however, would have to be able to manipulate objects of any shape, left in any location. Today, commercially available robots don't have anything like the dexterity of the human hand. Students in the Learning and Intelligent Systems Group at MIT's Computer Science and Artificial Intelligence Laboratory have demonstrated how household robots could use a little lateral thinking to compensate for their physical shortcomings. MIT senior Annie Holladay describes how her algorithm helps the robot adapt by using both of its arms instead of just one.

Topics:
Automation Computers Electronics & Computers Motion Control Robotics

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    Transcript

    00:00:05 Traditional robots work in very constrained and specific environments right now, and what I'm trying to do with this research is have the robot think about different ways it can fail before it even starts doing something and then adjust its environment to cope with those failures. So for example when you're trying to place an object that tips over very easily because it's got a very small base, or it is very tall, you want to be able to use your other hand to help you place the object. Our algorithm can identify the fact that this tower, that we see here, will tip over and use

    00:00:34 the other hand to help guide dropping that tower. So you can imagine any robot that's working in a household environment needs to be able to deal with objects that are weirdly shaped, or that it has grasped weirdly. And how my algorithm might be useful, is that you can now reliably place these objects instead of having a failure.