A Review of Children, Humanoid Robots and Caregivers

(Arsenio, 2004)

COM3240 – Week 3

Presented by Gizdem Akdur

A learning framework for a humanoid robot

Human-robot interactions The importance of a human actor Teaching humanoids as children

Inspired by cognitive development of a child Dependence on mother Awareness of his/her own individuality Self-exploration of his/her surroundings

Implementation of concepts on the humanoid robot Cog

Inspiration from Mahler’s child development theory

Margaret Mahler (1897-1985) – Hungarian physician and psychoanalyst with a main interest in mother-infant duality and childhood development

Was influenced by Freud and Piaget

Developed the Separation-Individuation Theory of Child Development (1979)

Mahler’s theory (1979)

Autistic Phase (from birth – 1 month old)

Symbiotic Phase (until around 5 months old)

Separation and Individuation Phase Differentiation (5-9 months) Practising (10-18 months) Re-approximation (15-24 months) Individuality and Object Constancy (24-36 months)

Learning on the Autistic and Symbiotic Phases

Autistic phase The newborn is mostly in a sleeping state. Awakens to eat and

satisfy other necessities Motor skills mainly consist of primitive reflexes

Symbiotic phase Infant’s attention dropped to repeatedly moving objects and to

sudden changes of motion Repetition helps Motivated the design of algorithms for detection of events

Object Segmentation algorithm extending the algorithms of previous studies – Arsenio, 2003 and

Fitzpatrick, 2003

Help from a human tutor

will guide the robot learning about its physical surroundings Correlate data among its own senses Control and integrate situational cues from its surrounding world Learn about out-of-reach objects and the different representations

they might appear

therefore special emphasis will be placed on social learning along a child’s physical topological spaces

robot executes a simple learned task (waving), and associates the sound to the movement of its own body

Physical topological spaces

(1) the robot's personal space, consisting of itself and familiar, manipulable objects(2) its living space, such as a bedroom or living room(3) its outside, unreachable world, such as the image of a bear on a forest

(1) Learning about objects and itself

Strategy described for a robot to associate data from several resources from its own senses from its senses and information stored on the world/robot’s

memory

3 main schemes to be implemented Cross-modal data association Object recognition Educational activities

(1.1) Cross-modal data association Extracting visual and audio features – patches of pixels and

sound frequency bands. The algorithm was therefore extended to detect both

Identification of robot’s own acoustic rhythms and the visual recognition of robot’s mirror image

Child and robot looking at a mirror, associating their image to their body (image/sound association for the robot has been amplified)

(1.2) Object recognition

A recognition scheme for objects (other than the robot’s body part) with 3 independent algorithms Colour Luminance Shape

Geometric hashing for high-speed performance Adaptive Hash Table was implemented

Object recognition and location in a computer generated bedroom. Scene lines matched to the train are outlined.

(1.3) Learning from educational activities

Corresponds to child’s practising (10-18 months) developmental sub-phase towards re-approximation (15-24 months) sub-phase

Robot learns object properties not only through cross-modal data correlations, but also by correlating human gestures and information stored in the world structure or on its own database

Object recognition algorithm applied to extract correlations between sensorial signals perceived from the world and geometric shapes present in such world

(2) Learning the world structure of the robot’s physical surroundings

Determining where objects should be stored based on probability of finding them on that place later If a book is placed in the fridge, the robot will hardly

find it!

The framework, developed to capture knowledge stored in robot’s surrounding world, consists of: (1) Learning 3D scenes from cues provided by a human

actor (2) Learning the spatial configuration of the objects

within a scene

(2.1) Learning about scenes

The environment surrounding the robot provides additional structure that can be learned through supervised learning techniques Defining scenes as a collection of objects with an uncertain

geometric configuration, each object at a minimum distance from another

Segmentation error analysis for furniture items on a scene – samples also shown

(2.2) Learning about objects in scenes Humanoids (like children) need to learn the relative probability

distribution of objects in a scene Constraining the search space is important to optimise computational

resources

Contextual features incorporate functional constraints Wavelet transformation (Strang and Nguyen, 1996) used

Holistic representation of the scene Main spectral characteristics of a scene encoded with a rough description

of its spatial arrangement

Reconstruction of the original image by the Wavelet transform. An holistic representation of the scene.

(3) Learning about the outside world through books Books are useful to teach different object representations and to

communicate properties of unknown objects to them Human-robot interactions are very essential at this stage. A human

tutor does the job of a mother of a child who teaches from books by tapping on the book’s representations

Segmentation by demonstration algorithm used

(3.1) Matching multiple representations

Object representations obtained from a book are put into a database for future recognition tasks

Methods were developed to establish a link between an object representation and real objects from surroundings using the object recognition technique

The framework can be applied on paintings, prints, photos and computer generated objects

Object recognition helps with the recognition of similar shapes with different colours but same geometric contours

Conclusion

A developmental object perception framework has been described which aims to teach humanoids as children

The epigenetic principle taken as a foundation

Robot learned about its surrounding world by building scene descriptions of world structures Contextual selections by using probabilities Storing information about object shapes for later use

The learning process with the guidance of a human tutor is essential to help the humanoid through its cognitive development

Thanks for listening