Understanding mirror neurons : A bio-robotic approach

1. Understanding mirror neurons: A bio-robotic approach 인지과학 협동과정 VCC 석사 2학기 김시온

2. 1.Introduction Understanding action 사과 때리다 왜? 미워서, 웃겨서, 짜증나서 까불어서, 사랑의 마음으로 잘되라고, 말 안들어서… -> 의도 파악의 필요성

3. 1.Introduction Speech understanding machine • Alvin Liberman(May 10, 1917 - Jan. 13, 2000) Analyzing the acoustic characteristics of spoken words Subjects Context Failed because of the limited computational power available Articulatory gestures Sound

4. 1.Introduction Acoustically –evoked motor “Resonance” Production에 관여한 지식들이 Perception에도 관여한다. Action과 관련하여 이러한 역할을 하는 것으로 보이는 영역이 발견되었다!!

5. 2. Physiological properties of monkey rostroventral premotor area (F5) Inferiorpremotor area

6. 2. Physiological properties of monkey rostroventral premotor area (F5) Motor Neurons • Goal-Directed action들에 반응 • (grasping, manipulating, tearing, holding) • -비슷한 손동작이 사용될지라도 목적이 • 다르면 발화하지 않음 • 많은 Grasping neuron들은 특정한 종류 • 의 Grasping에만 선택적 반응 • (precision, finger prehension, whole hand grasping) -> a repository of motor actions

7. 2. Physiological properties of monkey rostroventral premotor area (F5) Visuomotor Neurons- Canonical Neurons • 움직임이 있기 전에, 움직일 수 있는 대상을 보았을 때에도발화 • 많은 canonical neurons들은 하나, 혹은 소수의 대상에 선택적으로 반응한다.

8. 2. Physiological properties of monkey rostroventral premotor area (F5) Visuomotor Neurons- Mirror Neurons • 내가 행동할 때 뿐만 아니라, 다른 행위자가 동일한 목표-지향적 행동을 할 때에도 발화 • 최근 연구 결과 F5 이외에도 PF에서도 발견됨 • 다른 행위자가 손이나 입을 이용해 물체와 상호작용할 때 활발하게 반응 • 도구를 이용했을 때에는 반응하지 않음 • 대부분의 Mirror Neuron들은 한 종류의 행동에만 선택적으로 반응 (Highly congruent neurons)

9. 2. Physiological properties of monkey rostroventral premotor area (F5) Visuomotor Neurons- Mirror Neurons

10. 3. A model of area F5 and the mirror system A role of F5 Visual appearance of the hand Object affordance Classifier Controller

11. 3. A model of area F5 and the mirror system Controller-predictor formulation Acting Specific Motor Plan Comparison F5 forward information ≒ Sensory feedback(VMM) Desired action, timing Desired action Context ※ Feedback : to counteract disturbances and to learn from mistakes

12. 3. A model of area F5 and the mirror system Controller-predictor formulation Transforming visual cues into motor information as before Observing an action Mirror Neurons Bayesian approach??

13. 3. A model of area F5 and the mirror system Ontogenesis of mirror neurons • 아기들이 행위자의 목표를 판단할 때 사용하는 Cue는? • (Path? Object?) • Results • 6,9 mos : new>old goal Woodward(1998)

14. 3. A model of area F5 and the mirror system How do we achieve an action recognizing? Specific goals Canonical Neuron Certain actions are more likely to be applied to a particular object Objects are used to perform actions Linking Particular action = particular effect Mirror Neuron

15. Question!! • 과연 VMM 정보가 Action 인식에 도움을 줄까?? • 로봇를 가지고 한번 직접 실험해 보자!

16. 4. A machine with hands Motor-kinesthetic information + visual information vsonly visual information Recording “motor awareness” • Cyber-glove, a pair of CCD cameras, • a magnetic tracker, two touch sensors • Data was sampled at frame rate, • synchronized and stored to disk • by a Pentium class PC • Cyber-glove has 22 sensors • , recording the kinematics of the • hand at up to 112Hz • Two touch sensors were mounted on • the thumb and index finger to detect • the moment of contact with the object • Matlab was employed for post-processing

17. 4. A machine with hands Grasping type followed Napier’s taxonomy

18. 4. A machine with hands • Each grasping action was recorded • from six different subjects. • Moving the cameras to 12 different • locations • Images of the scene from the two camera • synchronized with the cyber-glove • and magnetic tracker data • The visual features were extracted from • pre-processed image data • Using a simple color segmentation algorithm • The data set was filtered through Principal • Component Analysis => Bayesian Classifier Classification Task Motor-kinesthetic information + visual information vsonly visual information

19. 4. A machine with hands VMM • Using a simple back-propagation neural network with sigmoidal units • The input of the VMM was the vector of the mapping of the images onto the space spanned by the first N PCA vectors • The output was the vector of joint angle acquried the data glove.

20. 4. A machine with hands Experiment 1 - Classification > -> 운동정보가 Action recognition에 도움을 줄 수 있다!!!

21. 5. Robotic experiment Cog Flipper A mirror neuron-like representation could be acquired by simply relying on the information exchanged during the robot- environment interaction (로봇-환경 간의 상호작용에서 mirror-neuron과 같은 표상을 획득할 수 있는가?)

22. 5. Robotic experiment Causal indirection, brain area and function 1 If any neural unit is active in these two situations(both when acting and observing) Then it can be regarded in all respects as a “mirror” unit. byrepetitive exposure and manipulation

23. 6. Learning object affordances Rolling affordances Grasping(x) Touching, poking, prodding, sweeping(o)

24. 6. Learning object affordances Rolling affordances

25. 6. Learning object affordances Generic poking actions Can this information be re-used when acting to generate anything useful Showing exploitation of the object affordance? 100/15 … 3번만 완전 실패( ≒ Canonical Neuron) Another interesting question is whether knowledge about object directed actions can be reused in interpreting observed actions performed by a human experimenter.

26. 7. Developing mirror neurons mimicry Same segmentation procedure section 6 generated by human and robot … LEARNING??

27. 7. Developing mirror neurons Passive vs Active imitation Demiris & Hayes, 2002

28. 7. Developing mirror neurons mimicry Learning of mirror neurons can be carried out by a process of autonomous development

29. 7. Conclusion • The motor information plays a role in the recognition process • A mirror-like representation can be developed autonomously on the basis of the interaction between an individual and the environment

30. 감사합니다.