Resilient Machines Through Continuous Self-Modeling Pattern Recognition Seung-Hyun Lee Soft Computing Lab. Josh Bongard,Victor Zykov, and Hod Lipson, Science, Vol.314, pp , 2006.

S FT YONSEI UNIV. KOREA 16 Contents Introduction Motivation Self Modeling Experiments Conclusion 1 / 15

S FT YONSEI UNIV. KOREA 16 Introduction Animals –After injured, create qualitatively different compensatory behaviors Robots –How robots can deal with this sort of unexpected damage?  self modeling 2 / 15

S FT YONSEI UNIV. KOREA 16 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 3 / 15

S FT YONSEI UNIV. KOREA 16 Self Modeling 4 / 15 Overall Process Modeling Prediction Testing

S FT YONSEI UNIV. KOREA 16 Self Modeling 5 / 15 Testing In this process –Performs an arbitrary motor action –Records the resulting sensory data

S FT YONSEI UNIV. KOREA 16 Self Modeling 6 / 15 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%)

S FT YONSEI UNIV. KOREA 16 Self Modeling 7 / 15 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

S FT YONSEI UNIV. KOREA 16 Self Modeling 8 / 15 After self modeling procedures(16 times repetition) –Create desired behaviors (D) –Execute by the physical robot

S FT YONSEI UNIV. KOREA 16 Self Modeling 9 / 15

S FT YONSEI UNIV. KOREA 16 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 10 / 15 Robot

S FT YONSEI UNIV. KOREA 16 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 11 / 15 Design

S FT YONSEI UNIV. KOREA 16 Experiments 12 / 15 Result

S FT YONSEI UNIV. KOREA 16 Experiments 13 / 15 Result  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

S FT YONSEI UNIV. KOREA 16 Experiments 14 / 15 Result  Automatically generated self-model was sufficiently predictive to allow the robot to consistently develop forward motion patterns without further physical trials

S FT YONSEI UNIV. KOREA 16 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? 15 / 15 Result

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