Post on 11-Jan-2016
Resilient Machines ThroughContinuous Self-Modeling
Pattern Recognition
2010.04.06
Seung-Hyun Lee
Soft Computing Lab.
Josh Bongard,Victor Zykov, and Hod Lipson, Science, Vol.314, pp. 1118-1121, 2006.
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Contents
• Introduction
• Motivation
• Self Modeling
• Experiments
• Conclusion
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Introduction
• Animals– After injured,
create qualitatively different
compensatory behaviors
• Robots– How robots can deal with this sort of unexpected damage?
self modeling
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Motivation
• How can robot learn its own morphology?– Direct observation?– Database of past experience?
• How can robot synthesize complex behaviors or recover from damage?
– Trial and error? slow, costly, risky!
• In this paper,– Inferring morphology: self-directed exploration– Complex behavior or recovering from damage: synthesize new be-
haviors using the resulting self models
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Self Modeling
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Overall Process
ModelingPrediction
Testing
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Self Modeling
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Testing
• In this process– Performs an arbitrary motor action
– Records the resulting sensory data
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Self Modeling
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Modeiling
• Model synthesize component – Synthesizes a set of candidate self-models
• Method– Before damage(topological modeling)
• Greedy random-mutation hill climber algorithm• 16 parameters
Robot initially knows how many body pars it is composed of, the size, weight and mass of each part, and angle-movement relations
• 15 random models• 200 iterations• Evaluation:
Euclidean distance between the centroid and where the centroid should be
– After damage(parametric modeling)• Self-model is frozen• 8 parameters (volumes and masses are scaled by 10%~200%)
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Self Modeling
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Prediction
• Action synthesize component– Find a new action most likely to elicit the most information from the
robot based on the current self model inferred
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Self Modeling
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• After self modeling procedures(16 times repetition)– Create desired behaviors (D)– Execute by the physical robot
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Self Modeling
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Experiments
• Speculation– 4 upper and lower leg parts and a main body– 8 motorized joints(-90 ~ 90 degree range)
• 0 degree: flat• Positive degree: upwards• Negative degree: downwards
– 2 tilt sensors
• Self model representation– Planar topological arrangement
• Damage– Disabled one leg
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Robot
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Experiments
• Control variables– Computational efforts(250,000 internal model simulations)– Physical actions(16)
• Three algorithms– Algorithm 1:
16 random physical actions batch training(modeling)– Algorithm 2:
Physical actions self modeling random action selection– Algorithm 3(proposed):
Physical actions self modeling actions selection
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Design
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Experiments
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Result
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Experiments
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Result
Model-driven algorithm is more accurate than ran-dom baseline algorithms
A robot that actively chooses action on the basis of its current set of hypothesized self-models has a bet-ter chance of successfully inferring its own morphol-ogy
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Experiments
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Result
Automatically generated self-model was sufficiently predictive to allow the robot to consistently develop forward motion patterns without further physical tri-als
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Conclusion
• Contribution– First physical system
• Autonomously recover its own morphology with little prior knowledge• Optimize the parameters of its morphology after unexpected change
– Show the possibility of unknown cognitive process• Which organisms actively create and update self models in the brain?• How and which sensor-motor signals are used to do this?• What form these model take?• Does human utilize multiple competing models?
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Result
Thank you