Social Robot Partners: Still Sci-fi?

8/6/2019 Social Robot Partners: Still Sci-fi?

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Social Robot Partners: Still Sci-fi?Kadir Firat Uyanik

KOVAN Research Lab.

Dept. of Computer Eng.

Middle East Technical Univ.

Ankara, Turkey

[email protected]

AbstractDesigning a man-made-man has always beenone of the most exciting dreams of humankind. It hasattracted many scientists, engineers and inquisitive peoplethrough the history of technology. Particularly in the lastdecade, many roboticists have shifted their fields of interestfrom robotic manipulation and navigation to humanoid

science (e.g. human-robot interaction, social robots, robotlearning etc.). Although computational power, sensor tech-nology and production techniques advanced a lot, the worldis still waiting for the first heartbeat signal of a robot beingable to recognize itself and its environment, walk aroundwithout falling over, communicate with people, do daily-lifetasks for/with people and learn how to behave properlyin an unanticipated situation. However, it is obvious thatrobotics still has a long way to go. The question is Howmuch complicated can it really be?.

I. INTRODUCTION

A. Historical Notes

Artificial humans, human-shaped mechanisms andhuman-like automata are nothing new for mankind. The

Greek myths, such as Hephaestus and Talos, talks about

golden robots and bronze human machines. A Chinese

artificer Yan Shi designed a mechanical handiwork [1]

which is able to sing and act, BC 1000s. In the eighthcentury, the Muslim alchemist, Jabir ibn Hayyan (la-

tinized as Geber), gives recipes of artificial slave humans

in his Book of Stones based on the ultimate goal,takwin1.

Ebul Iz (=Al-Jazari) is known as the creator of the first

programmable humanoid robot, 1206 [2]. His mecha-nism was a programmable drum machine consisting of

four automatic musicians in a boat floating in a lake to

entertain guests during royal drinking parties. Melodyof the music is changed by moving pegs in what may

be called programming. According to Charles B. Fowler,

more than fifty facial and body actions can be generated

during each musical selection[3]. Leonardo Da Vinci

1The act of takwin is an emulation of the divine, creative and life-giving powers of God.

Fig. 1. First programmable humanoid robotic system

designed a humanoid automaton, 1495. Leonardos robotwas capable of doing humanlike movements such as,

sitting up, moving its arms and neck, and anatomically

correct jaw. Late in the 1700s, Wolfgang von Kempelen

built the Turk, a chess-playing humanoid automatoncontrolled by a human staying inside the cabinet. In

the same century, Jacques de Vaucanson built The Flute

Player, a life-size figure of a shepherd, and also The

Tambourine Player that plays a flute and a tambourine.

Pierre Jaquet-Droz, his son and Jean-Frederic Leschot

built the Musician, the drawer and the writer which are

controlled by operators so as to realize some basic works,

such as playing an instrument, drawing a womans

picture and writing 40-letter long texts.

Throughout years of study, humanoid robots became

more and more complicated. After the mid of the 20thcentury, many theoretical models of biped locomotion

are suggested and the first active anthropomorphic ex-

oskeleton was built [6] at the Waseda University in

Tokyo. During 90s, there were many humanoid robots

like famous ASIMO (Advanced Step in Innovative MO-

bility)being able to walk on two legs and even run fairly

enough, or like the robot Cog being designed by Rodney

Brooks from MIT which is intended to emulate human


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Fig. 2. Left:Leonardos robot, a knight. Right:Reconstruction of theTurk, a Chess playing humanoid robot controlled by a human operator

thought and learn how to behave by experiencing the

world as we humans do.Today, robotics community tries

to make robots more social, more dexterous and more

mobile, or in short, much more humanlike.

B. Converging to human day by day

Sixty five years ago, there was only one working

computer in the world. A computer to debug a program

of which you should open it up and walk inside (see The

first computer bug, in the collections of the US Naval

Historical Center). More strangely, people of that time

have confidently predicted that United States will only

need six of these machines which is certainly not the

case today.

Just 40 years after the first computer, we got robots,

at least, working in the factories where environment is

well structured and working space is fully under control.

Nevertheless, RoboCup2, aims to build a team of fullyautonomous humanoid robot soccer players that shall

win the soccer game, comply with the official rule of

the FIFA, against the winner of the most recent World

Cup, by 2050. This may imply that we can have robot

partners, companions and assistants in 40 years, like we

have computers, laptops and pdas today.

The rest of this article examines how scientists cope

with the issues related to the humanlikeness of the robots

mainly in terms of appearance and intelligence.

I I . TO BE HUMANLIKE

Information technology has made a remarkableprogress recently. Internet, networking and communi-

cation advanced, and the form of communication and

social life changed considerably. Until now, robots have

been to oceans[27] and volcanoes[28]. Theyve become

2one of the most well known annual robotics competition that wasstarted in 1997, see for detailed information www.robocup.org

helicopters performing inverted-flight [10], and even

been to Mars[29]. As a next step, they will enter probably

the most sophisticated environment, our living rooms (!).

They should not only act on physical objects around

them, but also interact with people. They should be

capable of not only doing things for us, but also with us,

which necessitates generating proper actions in unantic-ipated situations and understanding of human believes,

desires, and physical actions. Hence, those robots should

setup a human-centric communication, move around

the environment particularly designed for people, make

sense of what they see, hear and touch, and learn doing

things in a social manner.

A. Appearance and Interactive Behaviors

People try to make animals, plants or even inanimate

objects talk, walk, see and think or in a way pretend

to behave like a human. There are many examples of

this attempt in the movies (iRobot, Artificial Intelligence,Transformers, Wall-e, Short Circuit etc.), cartoons (Irona

in Richey Rich, Rossie in Jetsons), toy designs, adver-

tisements but more seriously in robotics science.

There are several concerns about the humanlikeness of

the robots, like appearance and behavior. To tackle with

these problems two different approaches are necessary.

One is from a robotics science point of view, that is

building humanlike robots based on the knowledge from

cognitive science. The other one is from a cognitive

science which uses robots to verify hypotheses to under-

stand humans. This interdisciplinary framework is called

Fig. 3. Ishiguro and his android twin

as android science[9] by many Japanese roboticists, like

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Prof. Hiroshi Ishiguro, whose lab has child and adult

sized androids including his electromechanical twin,

figure 3. This robot, Geminoid[11], is not able to walk or

create complicated movements autonomously. It is tele-

operated by Ishiguro since AI technology is inadequate,

for now, to create humanlike conversations. As it can

be seen from the figure 4, captured sound and the lipmovements are encoded and transmitted through internet

to the Geminoid server. Server maintains the state of

the conversation and generates necessary outputs by

evaluating the input data packet and the state of the

conversation. It also generates unconscious behaviors

such as breathing, blinking and other hand and head

movements.

Ishiguro investigates the followings by developing this

robot;

How we define humanlikeness,

What human existence and presence mean,

How recognition mechanism works in human brain,

Is intelligence or long term communication crucial

factor in overcoming the uncanny valley.

Fig. 5. Simplified version of the figure in [13].

Uncanny valley is a hypothesis introduced by

Masahiro Mori in 1970 [13]. It represents a revulsion

among human observers which happens when robots

and any other thing resembling to humans act almost

but not entirely like actual humans.To bridge this valley,

robots behaviors and communication capabilities should

be as familiar as possible to the humans. An interac-

tion that increases familiarity and makes communication

smoother is called as social interaction [14]. Although

it seems unnecessary, humans chat with each other to

accomplish tasks. This interaction may not have an

explicit purpose of information exchange, but it serves as

the basis of smooth communication. Thats why, humans

will tend to prefer more familiar, in a way more social,

robots as their partners among the robots having identical

functionalities.

Fig. 6. The Robot Robovie is managing a conversation (adapted fromB. Mutlu et al. 2009)

A communication robot should have capabilities that

androids dont have yet. Firstly, this robot should be self-

contained in terms of its actuation mechanisms, which

makes communication more effective. This means, there

shouldnt be any wire or other links that prevents robot

from moving around, or communication mechanisms like

speakers or microphones outside of the robots body,

again limit its travel area. Haptic communication is also

important which makes communication more familiar

requiring touch sensors on the body of the robot.Communication robots are supposed to serve various

kinds of informational tasks (e.g.museum guide, infor-

mation booth personnel, shop assistant etc). This requires

enhanced communication skills like managing turn-

taking behavior and performing appropriate listening

behavior. During conversation, people switches between

different participant roles, such as speaker and addressee.

However, it is fairly possible that there might be other

side participants and also non-participant bystanders and

over-hearers. Although communication robots are still

not capable of recognizing speech robustly and generat-

ing speech adaptively, it is proved that gaze behaviors

play an important role in establishing and maintainingthose conversational roles [15].

B. Intelligence and Learning

A humanoid robot should be able to adapt itself to

the dynamically changing circumstances and it should

also be a quick learner to be useful in human populated

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Fig. 4. Overall system and data flow in Geminoid System[12].

environments. The degree of intelligence of a robot

is generally understood as how much successful it is

in a task or several tasks. For an intelligent robot,

achieving a goal or accomplishing a task depends on its

perception, decision making and actuation capabilities.

Although actuation is not totally solved yet, like in the

robot ASIMO ( it has zero-moment point theory based

control and non-regenerative actuation system -highly

inefficient-) or the robot Petman[16] ( it is the first

robot moving dynamically like a real person with its

heel-toe type walking pattern yet having a combustion

engine -improper for indoor environments-), perception

and learning are not even that much promising.

1) Perception: Humanoid robots should be aware of

themselves, they also should get necessary information

from the outside world to behave successfully. Today self

awareness can be mimicked by using several sensors,

such as motor encoders, force and tactile sensors, poten-

tiometers etc. to obtain proprioceptive information; gyro-

accelerometer couples are used to get the information

about posture alignment, microphones are for audial in-

formation. Stereo cameras and other superhuman sensors

( infrared range cameras or ultrasonic range finders) are

used as vision sensors.

The problem is, robots never understand what they

sense. They pretend as if they sensed by utilizing the

algorithms that are just the interpretations of the roboti-

cists. Unfortunately, scientists still dont know how ex-

actly human brain interprets the electrical signals which

are similar to the numerical values that robots obtain

from their sensors.

Robot vision is one of the major problems in per-

ception. An example is grasping of novel objects that

are seen for the first time. Stereo cameras are not

good enough if the objects are textureless or they

are transparent. Time of flight range cameras are low

resolution sensors, and laser range finders necessitates

too much time to scan 3D. Although stereopsis or 3D

reconstruction works well, only visible portions of the

object can be reconstructed. One solution is not to try

to obtain whole 3D representation of the object but

learn how to use partial shape information to find an

optimum grasping point [17], [18], [19] by computing

and evaluating several features, such as contact area,

contact symmetry, force closure and so on.

However, another problem with objects is the un-

derstanding of their permanence. Human infants obtain

knowledge of their environment by interacting with the

objects. One of the milestones in developing this ability

is learning the permanence of objects or conception

of physical causality. That is, knowing the continuity

of the existence of an object even it is occluded by

other objects. This requires extracting information about

an object depending on the state of its environment.

Recently, a model of situation-dependent predictor is

proposed in [20]. This model consists of four majormodules, such as attention, environment, predictor se-

lector and motion predictor modules which are briefly

explained in figure 7.

Not only visual recognition but also audial recognition

has similar problems. One of the major problem is

discrimination of the sound source of interest (SoI) or

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Fig. 7. A model of physical causality perception. Attention module extracts the geometrical information of the object and its surroundings. Inenvironment module this information is self organized by Restricted Boltzmann Machine type network[21] and the next position of the objectis calculated by prediction module based on the current state of the object and its environment [20].

detection of sound source location (SSL). Although there

are several methods to detect SSL, such as receiver

operating characteristic analysis [30] or time-delay of

arrival based approaches [31], speech recognition and

mood detection is still a crucial problem in the human

communication partner case.

Researchers, in a way, neglects these problems, so

as to deal with higher level problems, by utilizing

teleoperation systems in which perception ability isdistributed to the environment by adding multi infrared

range camera setup (motion capture system) to obtain

more complete information about the object of interest,

or piezoelectric pressure sensors to the floor ground

to locate communication partners (e.g. addressee or

bystander partners during a conversation), or multiple

microphones to locate SoI, or remote control panels to

control some of the higher level behaviors of the robot,

like the robot Geminoid (see figure 4) and the Robonaut

of NASA [22].

2) Learning: Social robot partners are supposed to

work in environments designed in a human-centered

manner.That is, those robots will come up with highly

changing circumstances and they should adapt their ca-

pabilities according to those changes and add new skills

to their repertoire quickly. Today, robots are suffering

from the computational complexity of the perception

algorithms, long-time requiring task learning phase, low

generalizablity of the learned behaviors between dif-

ferent agents and between different tasks for the same

agent. To deal with those problems scientists proposed

several techniques;

a) Reinforcement Learning:

In reinforcement learning (RL), a robot is rewarded or

punished according to the results of its interactions with

the environment. Learning is done by finding a policy

of actions that maximizes the subsequent award. If we

define accomplishing a task as the properly generatedaction sequences, a robot -learning to achieve a goal-

actually learns what to do next in a particular state.

RL is successfully implemented on different plat-

forms, such as an autonomous helicopter which learns to

flight invertedly [10], a robot soccer team which learns

to keep the ball away from opponent robots[32], or a

humanoid robot learns how to play air-hockey against

a human opponent[33]. One difficulty with RL is that

the state-action space can be very large (slows down the

learning process and decreases the generalizability of the

learned tasks as well ), which is a usual case in highly

antropomorphic robots having large degree of freedom

body kinematics. A solution to this problem would be

manually defining or hard-coding some parts of the task

to be learn. For instance, in Atkesons work on air-

hockey playing, primitive behaviors are manually given

to the system which decreases the state-action space and

helps system to converge to the optimum action policy

much quicker.

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b) Affordance Learning:

J.J. Gibson introduced the concept of affordances em-

phasizing the relationships between the organism and

its environment[26]. Gibson claims that each action

needs only relevant perceptual feature for its execution

which can be supplied by dedicated filters -running

concurrently- to extract certain cues from the environ-ment. This results in an immense perceptual economy.

He also mentions that an affordance is relative to the

organism. For instance, a bowling ball is liftable for an

adult, yet it is not for a little child.

This concept has been studied by various research

groups, commonly in terms of learning of consequences

of a particular action [24] or learning of invariant prop-

erties of environments that afford a certain action[25].

According to the representation given in figure 8 affor-

dances can be used to estimate outcomes of actions, to

plan actions to accomplish a task or to recognize objects

and actions of others. This representation has been

applied to various problems, such as directly ground-ing symbolic planning operators to continuous sensory-

motor experiences[35], reaching goal-directed behaviors

from primitive behaviors by learning the effects of

actions on different objects [36], learning how to grasp

novel objects by learning local visual descriptors of good

grasping points [37], or learning traversability affordance

[38]. In the affordance study of Sahin et. al., affordance

is formalized as a nested triple of

(effect, (entity, behavior))

where entity represents the initial state of the envi-

ronment (directly perceived by the agent) before robotperforming the action, behavior is the mean by which

agent interact with the entity, and effect represents the

perceptual change of the entity ( including the object

of interest) after the behavior is applied. For instance,

a robot can obtain the relationship between a black-can

and the action it applied to this object as;

(lifted, (black-can, lift-with-right-hand))

In addition, if the same agent applies the same behavior

on a different can, let it be yellow, and obtains the same

effect, then it will generalize its representation as;

(lifted,

(can, lift-with-right-hand))

Here, perception of the color of the object -can in this

case- looses its importance when the behavior lift-with-

right-hand is to be realized which is an example of

perceptual economy. Hence, a robot,learning via affor-

dances schema, does not try to extract an object model

to plan actions upon, yet it obtains its own representation

of the world in terms of several features including

shape, orientation, color and many other relevant factors.

Robots experiences with the objects are categorized (e.g.

via support vector machines) so as to build higher level

symbols of the world.

Fig. 8. Encoding affordances as relationships between actions, objectsand effects [34].

c) Social Learning:

There are some useful mechanisms to transfer knowledge

between agents (biological, computational or robotic

autonomous systems), such as social learning, be-

havior matching, imitation[7] and programming by

demonstration[23]. For instance, humans rely on im-

itation or observational learning in social interaction,

mostly to broaden their behavior repertoire, coordinate

interactional characteristics and ground the understand-

ings of others behaviors in own experience.

Psychologists proposed different theories about how

imitation ( i.e. social learning), occurs in the hu-man infant. Three of them are active intermodal map-

ping(e.g. Meltzoff and Moore, 1983, 1994, 1997), asso-

ciative sequence learning(Heyes, 2001, 2005; Heyes and

Ray 2000) and the theory of goal directed imitation(

Wohlschlger et al., 2003). These theories explain how

matching behaviors are generated and correspondence

problem is bridged. Correspondence problem[8] is a cru-

cial problem in imitation which shows up when imitator

agent tries to find and execute sequence of actions, using

own embodiment, that are generated by a demonstrator

possibly having a dissimilar embodiment.

In robotics, one difficulty is the perception of the

counterpart. To overcome this problem, generally motion

capture systems are used to sense the movement of the

counterpart. However, obtaining the information about

the motion of the counterpart is inadequate, this data

should also be mapped to the robots frame of reference.

However, in this stage, the problem of what to imi-

tate emerges. There are studies enabling robots to per-

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Fig. 9. The problem of producing a behavior that matches anobserved one is due to the coding that represents observed and executedmovements. Things get more and more complicated if the agentshave different body kinematics. The picture is from the book RobotProgramming by Demonstration, by Sylvain Calinon.

ceive relevant aspects of the counterparts movements.

For instance, Breazeal and Scasselatis [39] work on the

robots Cog and Kismet includes the detection of human

faces and eyes, and following humans gaze direction.Those robots are also capable of recognizing human

facial expressions and emotional vocalizations. Billiard

and Schaals work [40]also shows how to segment rel-

evant actions, that is, starting and finishing instances of

the action to be matched.

Inferring the goal of the demonstrator is another diffi-

culty. Currently, researchers set goals by hand. For exam-

ple, Alissandrakis et. al.s work [41] shows how a robot

can be told to imitate at the path level, trajectory level

or end point level which correspond to imitation of

whole action, sub-goals only or goal only, respectively.

On the other hand, Billard et. al.s work [42] shows how

a robot can infer the demonstrators goal. Their robotextracts the invariants across each demonstrations ( e.g.

moving several different boxes by using left hand). The

robot starts to copy this behavior at the coarse level, by

replicating all the trajectory or path of the action, then it

obtains the crucial parts of the movement and it tries to

reach to the same results by using the actions that robot

has already know.

III. CONCLUSION

Considering four decades of research and eminently

promising results, social robot partners are not a matter

of science-fiction anymore. Due to its multi interdisci-

plinary nature, robotics benefits from the advancements

in social sciences and engineering which results in a

growing community and rapidly accumulating knowl-

edge. Today, many researchers believe that robotics is

at the edge of a revolution like the computers in 80s.

Although they have stronger groundings than Marvin

Minskys1 -former head of the AI Lab of MIT-, various

problems related to perception, control and learning

still makes us suspicious about having that dream robot

which is able to adapt itself to our highly dynamic envi-

ronment, understand and learn what we say, show, do and

even think in order to set up an intuitive communication.

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Social Robot Partners: Still Sci-fi?