Sensorimotor Interaction in a Developing Robot

1. Sensorimotor Interaction in a Developing Robot Metta, Sandini, Natale& Panerai

2. Introduction • Developmental principles based on biological systems. • Time-variant machine learning. • Focus on humanoid robots.

3. Previous Work (to 2001) • Some work in machine learning for robotics. • Collect Data -> Train Machine -> Control Robot • Off-line training, tweaked by hand. • Time-invariant

4. Principles of Development • Physiology problem; explain how something in biology works. • A system is built by adapting from initial simplicity. • Non-adaptive systems often fail in the real world. • Real adaptation is hard to create and harder to control.

5. Modularity vs. Integration • Complex systems decomposed into small parts. • Parts are studied in isolation. • Real world is not modular – newborns are already integrated at birth. • Not all ‘modules’ are functioning or at full capabilities. • All are matched and promote shift to more complex behaviours.

6. Learning by Example (or not) • Example based learning is difficult to get right. • Basically function approximation. • Too many parameters -> Overfitting • Good approximation, bad generalisation. • Too few -> Oversmoothing • Bad approximation, no ‘grasp’ of problem complexity.

7. Development • Control the complexity and structure of the learner. • Different from learning which controls parameters of the structure. • Better to start with a simpler system. • Training data has a cost – exploration. • Failure is not an option!

8. Development cont. • Initial reflex-like starting conditions bootstrap the system. • Gather data through action, but without incurring penalties. • Quality of data linked to how the system acts. • Perception is derived from action. • Not just sensory processing.

9. Neurons • Mirror Neurons • Found in the frontal cortex. • Activated when an action is performed and seen. • Canonical Neurons • Responsive to actions like grasping. • Also respond to seeing a graspable object.

10. Actions Build on Actions • Assume a limited set of skills and motor control abilities. • Build new abilities on top of old ones. • Learn -> Act -> Perceive (randomly)

11. A Perception of Causality • Actions must have consequences. • Relate movements to sensorial consequences. • Eye and head tracking first develops synchronisation, then tunes the amplitude of the movements.

12. Affordances • Objects are classified by what you can do to them. • Learn affordances by action. • Measure outcome at sensory level. • Grasping is learnt because possession is ‘good’.

13. Babybot • 12 degrees of freedom. • Cameras, microphones, inertial sensors. • Orienting and reaching toward objects based on vision or audition.

14. Babybot’s Development • Reflex grasping as the robot learns to control gaze direction. • Gradual mapping between sound, vision and grasping. • Performs better with initially restricted vision that develops.