resilient machines through continuous self-modeling pattern recognition 2010.04.06 seung-hyun lee...

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.

S FT COMPUTING @ YONSEI UNIV . KOREA16

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


Self Modeling

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Testing

• In this process– Performs an arbitrary motor action

– Records the resulting sensory data


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%)


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


Self Modeling

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• After self modeling procedures(16 times repetition)– Create desired behaviors (D)– Execute by the physical robot


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


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


Experiments

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Result


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


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


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

resilient machines through continuous self-modeling pattern recognition 2010.04.06 seung-hyun lee...

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