1. Metta, Sandini, Natale & Panerai

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

3.  Some work in machine learning for robotics.  Collect Data -> Train Machine -> Control Robot  Off-line training, tweaked by hand.  Time-invariant

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

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

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

14.  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.