Resilient Machines Through Continuous Self-Modeling

1. Resilient Machines ThroughContinuous Self-Modeling Josh Bongard,VictorZykov, and Hod Lipson, Science, Vol.314, pp. 1118-1121, 2006. Pattern Recognition 2010.04.06 Seung-Hyun Lee Soft Computing Lab.

2. Contents • Introduction • Motivation • Self Modeling • Experiments • Conclusion

3. Introduction • Animals • After injured, create qualitatively different compensatory behaviors • Robots • How robots can deal with this sort of unexpected damage?  self modeling

4. 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 behaviors using the resulting self models

5. Overall Process Self Modeling Prediction Modeling Testing

6. Testing Self Modeling • In this process • Performs an arbitrary motor action • Records the resulting sensory data

7. Modeiling Self Modeling • 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%)

8. Prediction Self Modeling • Action synthesize component • Find a new action most likely to elicit the most information from the robot based on the current self model inferred

9. Self Modeling • After self modeling procedures(16 times repetition) • Create desired behaviors (D) • Execute by the physical robot

10. Self Modeling

11. Robot 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

12. Design 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

13. Result Experiments

14. Result Experiments • Model-driven algorithm is more accurate than random baseline algorithms • A robot that actively chooses action on the basis of its current set of hypothesized self-models has a better chance of successfully inferring its own morphology

15. Result Experiments • Automatically generated self-model was sufficiently predictive to allow the robot to consistently develop forward motion patterns without further physical trials

16. Result 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?

17. Thank you