=Paper=
{{Paper
|id=Vol-1855/EUCognition_2016_Part12
|storemode=property
|title=The Mirror Self-recognition for Robots
|pdfUrl=https://ceur-ws.org/Vol-1855/EUCognition_2016_Part12.pdf
|volume=Vol-1855
|authors=Andrej Lucny
|dblpUrl=https://dblp.org/rec/conf/eucognition/Lucny16
}}
==The Mirror Self-recognition for Robots==
https://ceur-ws.org/Vol-1855/EUCognition_2016_Part12.pdf
The Mirror Self-recognition for Robots
iCubSim at the mirror
Andrej Lucny
Department of Applied Informatics
FMFI, Comenius University
Bratislava, Slovakia
lucny@fmph.uniba.sk
Abstract—We introduce a simple example of artificial system system emerge from the parallel course of simpler modules and
which aims to mimic the process of the mirror self-recognition - from their mutual interactions. Agent-Space Architecture is a
ability limited to very few species. We assume that evolution of software tool for building modular control systems of robots,
the species is reflected in the structure of the underlying control running in real-time. It is strongly based on ideas of Brooks'
mechanism and design modules of the mechanism, concerning its subsumption architecture [1] and Minsky's society model of
incremental development. On this example, we also demonstrate mind [6] [4]. Within the framework we define operation of
modular architecture suitable for such task. It is based on individual modules and connect various module outputs to
decentralization and massive parallelism and enables incremental various module inputs (many:many) regardless the fact that
building of control system which is running in real-time and
each module can operate at different pace. Such combining of
easily combines modules operating at different pace.
either really slow or really fast processes is possible due to all
Keywords—the mirror self-recognition; robot iCubSim; Agent- data produced by a module (producer) being written onto a
space architecture blackboard (called space) and processed later, when another
module (consumer) is ready to process them. These written
data remain on the blackboard until they are rewritten by the
I. INTRODUCTION same or another producer, or their time validity expires. Finally
Seeing in the mirror, we recognize ourselves. However the data exchange supports also hierarchical control and its
child less than eighteen months old is rather looking for incremental development, e.g. module has to define sufficient
another person behind the mirror. In nature, the mirror self- priority for written data – otherwise it would not be able to
recognition is present only in exceptional cases, for instance rewrite their previous value already written on the blackboard.
chimpanzees recognize themselves in the mirror, while cats do
not. Can a robot recognize itself in the mirror? And what is the IV. MECHANISMS
origin of the self-recognition ability? We follow Scassellati and
Hart [3], who suppose that a robot should recognize itself in the Now we can try to implement the system, using which we
mirror due to perfect correlation between body movement and are able to evaluate correlation between own body movement
the image seen in the mirror. Unlike them and like Takeno [8] and the seen image movement. We aim to implement a
we use a very simplified body model. working demo following biological relevancy of our approach,
based on knowledge about the above-mentioned species.
II. TESTBED
A. Body model (proprioception)
Using the simulator of the humanoid robot iCub [7] we The body model is provided by the iCubSim simulator, thus
added a camera shooting the area in the front of the monitor we need to implement just its control and monitoring from the
with the robot rendered image, we create a control system on blackboard. We implement a module motor which reads an
which we can examine how the mirror self-recognition intended joint position intention from the blackboard,
emerges from basic mechanisms. This enables us to establish communicates with the simulator (via yarp rpc protocol) and as
interaction between the virtual robot and human sitting at the a result, it writes proprioception to the blackboard which
monitor and later between the robot and its image reflected to represents a real joint position (Fig. 1). In parallel, the added
the camera by the mirror. The simulator has almost perfect camera regularly provides a new image to the blackboard.
model of the robot body. However for our purposes we intend
to simplify it, thus we move a single joint – the joint moving
head from side to side. In this way the body model is simplified B. Mirroring
to a single number – the angle of the robot head inclination. As we aim to compare the body model (i.e. value
proprioception) with the image captured by the camera, we
III. ARCHITECTURE need to get an analogical model from the image. This approach
is still biologically relevant since several species are proven to
For examination of mechanisms underlying the mirror self- obtain such ability (mirror neurons). (How such ability has
recognition process, we employ a modular framework (Agent- emerged and evolved, is not the subject of our exploration – it
Space Architecture [5]) which enables us to let the control is only limited to the implementation of this ability.) Since we
Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 46
take into account both interaction between the robot and human implement the active part of imitation, which can be called the
and interaction between the robot and its image in the mirror, invitation to imitation and obtains a higher priority than the
we combine two specific methods which output the same data passive imitation and occasionally generates side to side
onto the backboard – the model of the seen image (the model movement of the robot’s head when the body seen does not
value in Fig. 1).
move. However we found out that even slight inaccuracies in
the model evaluation are sufficient to induce the imitation
process.
D. Social modelling
Imitation process provides us with the data of correlation
between the body owned and the body seen. When an
individual lives within a society, it is important for him to
categorize the data and associate them with the image seen.
When such individual meets the mirror, it possibly creates a
Fig. 1. The modular structure of the mirror self-recognition system new category and finds out that the corresponding correlation
is unusually high. In fact, it is so high that the image seen can
not only be associated with the body model seen, but also with
its own body, meaning that it sees itself. Instead of modelling
the society, for our purposes, it is sufficient to record the data
produced by the two models since the aim of the study is to
differentiate the data created when robot encounters human
and when robot sees its own image in the mirror.
V. CONCLUSION
As a result of implementation of the above mentioned
mechanisms, when iCubSim's image is reflected into the
external camera by a mirror, robot invites himself to imitation
and it stays in an interaction with itself for certain amount of
time (Fig. 2). Thus correlation between the body model
(proprioception) and the model of the image seen (model) can
be evaluated and its unusually high value indicates that the
robot sees itself in the mirror. Thus the designed structure of
Fig. 2. The iCumSim robot following its own image in the mirror
the mirror self-recognition process is partially verified.
For the iCubSim image processing (we rotate a printed As a teaser video can be viewed here:
picture of iCubSim in front of the camera) we employ the http://www.agentspace.org/mirror/iCubSimAtTheMirror.mp4
SURF method. SURF provides projection of given template to
the image seen from which we can easily calculate inclination
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Proceedings of EUCognition 2016 - "Cognitive Robot Architectures" - CEUR-WS 47