309 research outputs found
Perceptual Control Theory for Engagement and Disengagement of Users in Public Spaces
This paper presents Perceptual Control Theory-a model that explains behaviour as an attempt to keep sensory inputs in a desired range and demonstrates that it can be used to develop an approach designed to make robots capable of human interaction. In particular, we present an approach that embodies the most salient features of the theory through a feedback loop. This approach has been implemented on a Pepper robot, and a preliminary experiment has been performed by deploying the robot in the entrance hall of a university building. The results show that the robot effectively engages and disengages the attention of people in 43% and 39% of cases, respectively. This result has been obtained in a fully natural setting where people were unaware of being involved in an experiment and therefore behaved spontaneously
Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning
The central problem in automated planning---action selection---is also a
primary topic in the dialogue systems research community, however, the
nature of research in that community is significantly different from that
of planning, with a focus on end-to-end systems and user evaluations. In
particular, numerous toolkits are available for developing speech-based
dialogue systems that include not only a method for representing states and
actions, but also a mechanism for reasoning and selecting the actions,
often combined with a technical framework designed to simplify the task of
creating end-to-end systems. We contrast this situation with that of
automated planning, and argue that the dialogue systems community could
benefit from some of the directions adopted by the planning community, and
that there also exist opportunities and lessons for automated planning
Natural language generation for social robotics: Opportunities and challenges
In the increasingly popular and diverse research area of social robotics, the primary goal is to develop robot agents that exhibit
socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face
social conversation is fluent, flexible linguistic interaction: as Bavelas et al. [1] point out, face-to-face dialogue is both the basic
form of human communication and the richest and most flexible, combining unrestricted verbal expression with meaningful
non-verbal acts such as gestures and facial displays, along with instantaneous, continuous collaboration between the speaker
and the listener. In practice, however, most developers of social robots tend not to use the full possibilities of the unrestricted
verbal expression afforded by face-to-face conversation; instead, they generally tend to employ relatively simplistic processes
for choosing the words for their robots to say. This contrasts with the work carried out Natural Language Generation (NLG), the
field of computational linguistics devoted to the automated production of high-quality linguistic content: while this research area
is also an active one, in general most effort in NLG is focussed on producing high-quality written text. This article summarises
the state-of-the-art in the two individual research areas of social robotics and natural language generation. It then discusses
the reasons why so few current social robots make use of more sophisticated generation techniques. Finally, an approach is
proposed to bringing some aspects of NLG into social robotics, concentrating on techniques and tools that are most appropriate
to the needs of socially interactive robots
Modulating the Non-Verbal Social Signals of a Humanoid Robot
In this demonstration we present a repertoire of social signals generated by the humanoid robot Pepper in the context of the EU-funded project MuMMER. The aim of this research is to provide the robot with the expressive capabilities required to interact with people in real-world public spaces such as shopping malls-and being able to control the non-verbal behaviour of such a robot is key to engaging with humans in an effective way. We propose an approach to modulating the non-verbal social signals of the robot based on systematically varying the amplitude and speed of the joint motions and gathering user evaluations of the resulting gestures. We anticipate that the humans' perception of the robot behaviour will be influenced by these modulations
Comparing User Responses to Limited and Flexible Interaction in a Conversational Interface
The principles governing written communication have been well studied, and well incorporated in interactive computer systems. However, the role of spoken language and in human-computer interaction, while an increasingly popular modality, still needs to be explored further [3]. Evidence suggests that this technology must further evolve in order to support more "natural" conversations [2], and that the use of speech interfaces is correlated with a high cognitive demand and attention [4]. In the context of spoken dialogue systems, a continuum has long been identified between "systeminitiative" interactions, where the system is in complete control of the overall interaction and the user answers a series of prescribed questions, and "user-initiative" interactions, where the user is free to say anything and the system must respond [5]. However, much of the work in this area predates the recent explosive growth of conversational interfaces
Using General-Purpose Planning for Action Selection in Human-Robot Interaction
A central problem in designing and implementing interactive
systems—action selection—is also a core research topic in
automated planning. While numerous toolkits are available
for building end-to-end interactive systems, the tight coupling
of representation, reasoning, and technical frameworks found
in these toolkits often makes it difficult to compare or change
the underlying domain models. In contrast, the automated
planning community provides general-purpose representation
languages and multiple planning engines that support these
languages. We describe our recent work on automated planning
for task-based social interaction, using a robot that must
interact with multiple humans in a bartending domain
A Reusable Interaction Management Module: Use case for Empathic Robotic Tutoring
We demonstrate the workings of a stochastic Interaction Management and showcase this working as part of a learning environment that includes a robotic tutor who interacts with students, helping them through a pedagogical task
Evaluating the impact of variation in automatically generated embodied object descriptions
Institute for Communicating and Collaborative SystemsThe primary task for any system that aims to automatically generate human-readable output
is choice: the input to the system is usually well-specified, but there can be a wide range of
options for creating a presentation based on that input. When designing such a system, an
important decision is to select which aspects of the output are hard-wired and which allow
for dynamic variation. Supporting dynamic choice requires additional representation and
processing effort in the system, so it is important to ensure that incorporating variation has a
positive effect on the generated output.
In this thesis, we concentrate on two types of output generated by a multimodal dialogue
system: linguistic descriptions of objects drawn from a database, and conversational facial
displays of an embodied talking head. In a series of experiments, we add different types of
variation to one of these types of output. The impact of each implementation is then assessed
through a user evaluation in which human judges compare outputs generated by the basic
version of the system to those generated by the modified version; in some cases, we also use
automated metrics to compare the versions of the generated output.
This series of implementations and evaluations allows us to address three related issues. First,
we explore the circumstances under which users perceive and appreciate variation in generated
output. Second, we compare two methods of including variation into the output of a
corpus-based generation system. Third, we compare human judgements of output quality to
the predictions of a range of automated metrics.
The results of the thesis are as follows. The judges generally preferred output that incorporated
variation, except for a small number of cases where other aspects of the output obscured
it or the variation was not marked. In general, the output of systems that chose the majority
option was judged worse than that of systems that chose from a wider range of outputs.
However, the results for non-verbal displays were mixed: users mildly preferred agent outputs
where the facial displays were generated using stochastic techniques to those where a simple
rule was used, but the stochastic facial displays decreased users’ ability to identify contextual
tailoring in speech while the rule-based displays did not. Finally, automated metrics based on
simple corpus similarity favour generation strategies that do not diverge far from the average
corpus examples, which are exactly the strategies that human judges tend to dislike. Automated
metrics that measure other properties of the generated output correspond more closely
to users’ preferences
Action Selection for Interaction Management: Opportunities and Lessons for Automated Planning
The central problem in automated planning---action selection---is also a
primary topic in the dialogue systems research community, however, the
nature of research in that community is significantly different from that
of planning, with a focus on end-to-end systems and user evaluations. In
particular, numerous toolkits are available for developing speech-based
dialogue systems that include not only a method for representing states and
actions, but also a mechanism for reasoning and selecting the actions,
often combined with a technical framework designed to simplify the task of
creating end-to-end systems. We contrast this situation with that of
automated planning, and argue that the dialogue systems community could
benefit from some of the directions adopted by the planning community, and
that there also exist opportunities and lessons for automated planning
- …