1,422 research outputs found
The RACE Project: Robustness by Autonomous Competence Enhancement
This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system
Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success
Purpose: Service robots are taking over the organizational frontline. Despite a recent surge in studies on this topic, extant works are predominantly conceptual in nature. The purpose of this paper is to provide valuable empirical insights by building on the attribution theory. Design/methodology/approach: Two vignette-based experimental studies were employed. Data were collected from US respondents who were randomly assigned to scenarios focusing on a hotel’s reception service and restaurant’s waiter service. Findings: Results indicate that respondents make stronger attributions of responsibility for the service performance toward humans than toward robots, especially when a service failure occurs. Customers thus attribute responsibility to the firm rather than the frontline robot. Interestingly, the perceived stability of the performance is greater when the service is conducted by a robot than by an employee. This implies that customers expect employees to shape up after a poor service encounter but expect little improvement in robots’ performance over time. Practical implications: Robots are perceived to be more representative of a firm than employees. To avoid harmful customer attributions, service providers should clearly communicate to customers that frontline robots pack sophisticated analytical, rather than simple mechanical, artificial intelligence technology that explicitly learns from service failures. Originality/value: Customer responses to frontline robots have remained largely unexplored. This paper is the first to explore the attributions that customers make when they experience robots in the frontline
Appearance-based localization for mobile robots using digital zoom and visual compass
This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally
Developing a socially-aware robot assistant for delivery tasks
This is a post-peer-review, pre-copyedit version of an article published in Applied Technologies. The final authenticated version is available online at:Â http://dx.doi.org/10.1007/978-3-030-42520-3_42This paper discusses about elements to be considered for developing a Service Robot that performs its task in a social environment. Due to the social focus of the service, not only technical considerations are demanded in order to accomplish with the task, but also the acceptance of use for the people, who interact with all of them. As our particular research topic, we establish a taxonomy to determine the framework for the development of socially-aware robot assistants for serving tasks such as deliveries. This is a general approach to be considered for any service robot being implemented in a social context. This article presents several previous cases of the implementation of service mobile robots, their analysis and the motivation of how to solve their acceptance and use by people. Therefore, under this approach it is very important not to generate false expectations about the capabilities of the robot, because as it is explained in the state of the art analysis that very high unsatisfied expectations lead to leaving the robot unused....Peer ReviewedPostprint (published version
Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker
Social Robotics and Human-Robot Interaction (HRI) research relies on
different Affective Computing (AC) solutions for sensing, perceiving and
understanding human affective behaviour during interactions. This may include
utilising off-the-shelf affect perception models that are pre-trained on
popular affect recognition benchmarks and directly applied to situated
interactions. However, the conditions in situated human-robot interactions
differ significantly from the training data and settings of these models. Thus,
there is a need to deepen our understanding of how AC solutions can be best
leveraged, customised and applied for situated HRI. This paper, while
critiquing the existing practices, presents four critical lessons to be noted
by the hitchhiker when applying AC for HRI research. These lessons conclude
that: (i) The six basic emotions categories are irrelevant in situated
interactions, (ii) Affect recognition accuracy (%) improvements are
unimportant, (iii) Affect recognition does not generalise across contexts, and
(iv) Affect recognition alone is insufficient for adaptation and
personalisation. By describing the background and the context for each lesson,
and demonstrating how these lessons have been learnt, this paper aims to enable
the hitchhiker to successfully and insightfully leverage AC solutions for
advancing HRI research.Comment: 11 pages, 3 figures, 1 tabl
Online Context-based Object Recognition for Mobile Robots
This work proposes a robotic object recognition
system that takes advantage of the contextual information latent
in human-like environments in an online fashion. To fully leverage
context, it is needed perceptual information from (at least) a
portion of the scene containing the objects of interest, which could
not be entirely covered by just an one-shot sensor observation.
Information from a larger portion of the scenario could still
be considered by progressively registering observations, but this
approach experiences difficulties under some circumstances, e.g.
limited and heavily demanded computational resources, dynamic
environments, etc. Instead of this, the proposed recognition
system relies on an anchoring process for the fast registration
and propagation of objects’ features and locations beyond the
current sensor frustum. In this way, the system builds a graphbased
world model containing the objects in the scenario (both
in the current and previously perceived shots), which is exploited
by a Probabilistic Graphical Model (PGM) in order to leverage
contextual information during recognition. We also propose a
novel way to include the outcome of local object recognition
methods in the PGM, which results in a decrease in the usually
high CRF learning complexity. A demonstration of our proposal
has been conducted employing a dataset captured by a mobile
robot from restaurant-like settings, showing promising results.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Artificial Intelligence and Robotics in Marketing
This chapter illustrates the role of artificial intelligence (AI) and robotics in marketing and will help managers develop a deeper understanding of its potential to revolutionize the service experience. We summarize the use of AI and robots in practice and show that the adoption of AI predominantly occurs at the task level rather than the job level, implying that AI takes over some tasks that are part of a job and not the entire job. Based on these insights, we discuss opportunities and drawbacks of AI and robots and reflect on whether service robots will complement or substitute human employees. Moreover, we explain why many consumers are still reluctant to engage with these new technologies and which conditions should be met in order to benefit from using service robots
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