This is pretty smart:

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Robots learn how to arrange objects by 'hallucinating' humans into their 
environment (w/ video)
June 21st, 2013 in Electronics / Robotics 

Credit: Cornell University

(Phys.org) -A team of robotics engineers working in the Personal Robotics Lab 
at Cornell University (led by Ashutosh Saxena) has developed a new way to give 
robots a context-sensitive way to organize a room. Instead of providing the 
robots with a map, the researchers instead cause the robot to "imagine" how a 
human being would use objects in a room and then to place them accordingly.

Traditional programming for robots has relied on providing clear instructions 
on what they are supposed to do-pick something up from one place and put it 
down in another, for example. To get a robot to engage in activities that 
require some degree of intuition, however, would mean giving them some means 
for doing so. One example would be to ask a robot to enter a room, note objects 
on a table, and ask that they be arranged on a desk for use by a person. To 
arrange objects in a way that makes sense to a human being requires some 
understanding of how people operate. To do that, the Cornell team gave a test 
robot a means for imagining what a person would look like in the room while 
using a set of objects.

As an example, the researchers programmed a robot to pick up a coffee mug and 
computer mouse from a table and place them on a desk in what would seem the 
most logical positions based on human behavior. To do that, they gave the robot 
what they call "an ability to hallucinate" humans into the room-the robot 
"brain" overlays images of stick figure humans onto images of the room. Various 
poses are considered while the robot "imagines"; how a human might make the 
best use of the mouse and mug. Based on this process, the robot then placed the 
mouse just to the right of the keyboard (because the average person is right 
handed) and the mug a little ways back-within reach, but not so close it might 
get knocked over unintentionally. The approach mimics what a human would likely 
do given the same instructions, of course, and that is exactly the point.

[Video available on website]

Hallucinating humans for robotic scene understanding.
The team will be outlining their research findings and progress at the 
Robotics: Science and Systems 2013 conference in Berlin next week.

More information: Project page: pr.cs.cornell.edu/hallucinatinghumans/

Research papers: 
Infinite Latent Conditional Random Fields for Modeling Environments through 
Humans (PDF) 
Hallucinated Humans as the Hidden Context for Labeling 3D Scenes (PDF)

via IEEESpectrum

© 2013 Phys.org

"Robots learn how to arrange objects by 'hallucinating' humans into their 
environment (w/ video)." June 21st, 2013. 
http://phys.org/news/2013-06-robots-hallucinating-humans-environment-video.html


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Or as the researchers themselves put it:

“For scene understanding, one popular approach has
been to model the object-object relationships. In this paper,
we hypothesize that such relationships are only an artifact
of certain hidden factors, such as humans. For example, the
objects, monitor and keyboard, are strongly spatially correlated only because a 
human types on the keyboard while
watching the monitor. Our goal is to learn this hidden human context (i.e., the 
human-object relationships), and also
use it as a cue for labeling the scenes. We present Infinite
Factored Topic Model (IFTM), where we consider a scene
as being generated from two types of topics: human configurations and 
human-object relationships. This enables our
algorithm to hallucinate the possible configurations of the
humans in the scene parsimoniously”

http://www.cs.cornell.edu/~asaxena/papers/jiang-hallucinatinghumans-labeling3d-cvpr13.pdf

____

Looking at objects and scnes in terms of how humans use and relate to them, is 
consistent with the Barsalou idea of concepts as simulators of how we relate to 
objects and scenes.

I of course v. much like these guys’ use of graphics/stick figs.. The 
limitations of their algorithmic approach, though, are that they can only use a 
limited set of figure poses, whereas the human mind’s figures are fluid and 
more or less infinitely reconfigurable, re-posable.


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AGI
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