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A Test Environment for Studying Robotic Eye Movements
rotation axis. With the exception of stand-alone vision systems (Samson et al.
2006), no other system so far has considered ocular torsion.
16.1.2 Motivation
In human–robot interaction, gaze-based communication with human performance on the robot side has a great advantage: humans do not have to learn
how to use an artificial interface such as a mouse or a keyboard, but they can
communicate immediately and naturally just like with other humans. This
is especially important for service robots who directly interact with people,
some of whom might be older or disabled.
To our knowledge, human likeness of robotic gaze behavior has not yet
been examined with a gaze-based telepresence setup. In contrast to the few
studies that have applied only simple and stereotyped eye movements, in
either virtual reality environments (Garau et al. 2001; Lee, Badler, and Badler
2002) or with real robots (MacDorman et al. 2005; Sakamoto et al. 2007), our
approach bears the potential to open up a new field by providing the whole
range of coordinated eye and head movements. Although in this work we
only present technical aspects of our experimental platform, our long-term
aim is not a telepresence system per se but rather to use it as a tool to identify
which aspects of gaze a social robot should implement. The reduction of
critical aspects and the omission of unimportant functionalities will eventually simplify the final goal of an autonomous active vision system.
Instead of building a model for robotic interaction from scratch, as has
been done, for example, in Breazeal (2003), Lee, Badler, and Badler (2002),
and Sidner et al. (2004), we use a top-down approach, in which we have a
complete cognitive model of a robotic communication partner (controlled
by a human). Then, we can restrict or alter its abilities bit by bit (see flash in
Figure 16.1) to elucidate the key factors of gaze and head movement behavior
in human–robot interactions.
16.1.3 Objectives
We provide two different modes for controlling the eye and head movements.
For scripted experiments, three-dimensional coordinates can directly be fed
to the robot. Also, laser pointers can be attached to the robotic eyes, thus
visualizing the lines of sight in both the real world and the scene camera. By
manually moving the eyes with the mouse, target points can be selected and
later played back by a script.
In the experimental Wizard-of-Oz setup outlined in Figure 16.1, the ability
to replicate the whole range of ocular motor functionality and to manipulate different aspects is mandatory for enabling examinations of gaze-based
human–robot interactions. The platform needs to provide human-like interaction capabilities and, in particular, the dynamics of the motion devices
A Test Environment for Studying Robotic Eye Movements
rotation axis. With the exception of stand-alone vision systems (Samson et al.
2006), no other system so far has considered ocular torsion.
16.1.2 Motivation
In human–robot interaction, gaze-based communication with human performance on the robot side has a great advantage: humans do not have to learn
how to use an artificial interface such as a mouse or a keyboard, but they can
communicate immediately and naturally just like with other humans. This
is especially important for service robots who directly interact with people,
some of whom might be older or disabled.
To our knowledge, human likeness of robotic gaze behavior has not yet
been examined with a gaze-based telepresence setup. In contrast to the few
studies that have applied only simple and stereotyped eye movements, in
either virtual reality environments (Garau et al. 2001; Lee, Badler, and Badler
2002) or with real robots (MacDorman et al. 2005; Sakamoto et al. 2007), our
approach bears the potential to open up a new field by providing the whole
range of coordinated eye and head movements. Although in this work we
only present technical aspects of our experimental platform, our long-term
aim is not a telepresence system per se but rather to use it as a tool to identify
which aspects of gaze a social robot should implement. The reduction of
critical aspects and the omission of unimportant functionalities will eventually simplify the final goal of an autonomous active vision system.
Instead of building a model for robotic interaction from scratch, as has
been done, for example, in Breazeal (2003), Lee, Badler, and Badler (2002),
and Sidner et al. (2004), we use a top-down approach, in which we have a
complete cognitive model of a robotic communication partner (controlled
by a human). Then, we can restrict or alter its abilities bit by bit (see flash in
Figure 16.1) to elucidate the key factors of gaze and head movement behavior
in human–robot interactions.
16.1.3 Objectives
We provide two different modes for controlling the eye and head movements.
For scripted experiments, three-dimensional coordinates can directly be fed
to the robot. Also, laser pointers can be attached to the robotic eyes, thus
visualizing the lines of sight in both the real world and the scene camera. By
manually moving the eyes with the mouse, target points can be selected and
later played back by a script.
In the experimental Wizard-of-Oz setup outlined in Figure 16.1, the ability
to replicate the whole range of ocular motor functionality and to manipulate different aspects is mandatory for enabling examinations of gaze-based
human–robot interactions. The platform needs to provide human-like interaction capabilities and, in particular, the dynamics of the motion devices