Engineers Create a Robot That Can 'Imagine' Itself (eurekalert.org)
Columbia Engineering researchers have made a major advance in robotics by creating a robot that learns what it is, from scratch, with zero prior knowledge of physics, geometry, or motor dynamics. Initially the robot does not know if it is a spider, a snake, an arm -- it has no clue what its shape is. After a brief period of "babbling," and within about a day of intensive computing, their robot creates a self-simulation. The robot can then use that self-simulator internally to contemplate and adapt to different situations, handling new tasks as well as detecting and repairing damage in its own body. From a report: The work is published today in Science Robotics. To date, robots have operated by having a human explicitly model the robot. "But if we want robots to become independent, to adapt quickly to scenarios unforeseen by their creators, then it's essential that they learn to simulate themselves," says Hod Lipson, professor of mechanical engineering, and director of the Creative Machines lab, where the research was done.
For the study, Lipson and his PhD student Robert Kwiatkowski used a four-degree-of-freedom articulated robotic arm. Initially, the robot moved randomly and collected approximately one thousand trajectories, each comprising one hundred points. The robot then used deep learning, a modern machine learning technique, to create a self-model. The first self-models were quite inaccurate, and the robot did not know what it was, or how its joints were connected. But after less than 35 hours of training, the self-model became consistent with the physical robot to within about four centimeters. The self-model performed a pick-and-place task in a closed loop system that enabled the robot to recalibrate its original position between each step along the trajectory based entirely on the internal self-model. With the closed loop control, the robot was able to grasp objects at specific locations on the ground and deposit them into a receptacle with 100 percent success.
For the study, Lipson and his PhD student Robert Kwiatkowski used a four-degree-of-freedom articulated robotic arm. Initially, the robot moved randomly and collected approximately one thousand trajectories, each comprising one hundred points. The robot then used deep learning, a modern machine learning technique, to create a self-model. The first self-models were quite inaccurate, and the robot did not know what it was, or how its joints were connected. But after less than 35 hours of training, the self-model became consistent with the physical robot to within about four centimeters. The self-model performed a pick-and-place task in a closed loop system that enabled the robot to recalibrate its original position between each step along the trajectory based entirely on the internal self-model. With the closed loop control, the robot was able to grasp objects at specific locations on the ground and deposit them into a receptacle with 100 percent success.
When you put it in exactly those terms, I'm forced to wonder, WHY THE FUCK DO WE WANT THAT?!?!
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Most of the time when I read a machine learning or AI story it seems fairly benign and reasonable. Other times I feel a pit form in the bottom of my stomach and wonder how close to the tipping point we our to creating our robot overlords.
----- obSig
Robots imagine it's self? Somebody has a vivid imagination..
I'm guessing it's not the robots...
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Sheesh .. I thought everyone knew that you shouldn't anthropomorphize machines .. they don't like it when you do.
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Lameness filter encountered. Post aborted! Filter error: Don't use so many caps. It's like YELLING.Lameness filter encountered. Post aborted! Filter error: Don't use so many caps. It's like YELLING.Lameness filter encountered. Post aborted! Filter error: Don't use so many caps. It's like YELLING.Lameness filter encountered. Post aborted! Filter error: Don't use so many caps. It's like YELLING.Lameness filter encountered. Post aborted! Filter error: Don't use so many caps. It's like YELLING.
"Prediction: within 10 years, Windows will be a Linux distribution." Me, 7-6-2016
This does not know what it is. It has just figured out parameters of the neural network that make it act according to a (human) model of its physics. That's not anything like self awareness. It's all numbers, plain math.
You mean a machine performed self-calibration? Welcome to 2019 style "engineering".
Sonny(iRobot)? The Robot(Lost in Space)? hal 9000(Space Odyssey )? Maximilian(The Black Hole)? âR2-D2/C3PO (Star Wars)?
Damn, I haven't even come close to touching all of the different robots that have previously been named.
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The article describes a robot that can model itself physically.
The more interesting exploration would involve the robot modelling its own internal state. At that point a closed feedback loop could be initiated with the model informing the system about itself which in turn informs and becomes part of the model.
If the model becomes good enough, the system might eventually develop the illusion that its embedded model is actually itself. At least that seems to be what happened with the majority of humans.
Robot: "Alexa, what am I?"
Table-ized A.I.
The paper is on sci-hub.tw. The DOI is 10.1126/scirobotics.aau9354. The source code is at https://github.com/rjk2147/Tas....
Quote from the paper: Actions correspond to four motor angle commands and sensations correspond to the absolute coordinate of the end effector.
This means that it cannot see, and either needs additional 3D motion tracking hardware or human handcrafted logic to detect the position of its end effector. Everything it does is just learn an inverse kinematics model, so that you can command it to move the end effector to a certain position afterwards. But it cannot learn, for example, to detect position and orientation of a target object or to avoid obstacles in its way.
Once we decide that an entity is conscious then yes, we should bestow such considerations, as any decent person does with fellow humans.