Comparing task-based and socially intelligent behaviour in a robot bartender

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

Gaschler

et al. 2014

Self Cite

Abstract-In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard speech recognition confidence score thresholds instead of belief tracking. The results suggest that users are served fewer incorrect drinks when the uncertainty is retained in the state.

Section: E Resultssupporting

confidence: 75%

Handling uncertain input in multi-user human-robot interaction

Keizer

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

Gaschler

et al. 2014

Self Cite

“…Also, recall that this questionnaire is aimed to evaluate an overall impression of the robot system, whereas our focus in developing this Nao-based version of the robot was on approximating the basic capabilities of the original JAMES robot and on replicating the previous comparison between the two SSE versions. Note that a decrease in Godspeed scores was also found in two recent user evaluations of the full JAMES robot system [9], [10]. …”

Section: A Godspeed Questionnairesupporting

confidence: 56%

Evaluating a social multi-user interaction model using a Nao robot

Keizer

Kastoris

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

et al. 2014

Self Cite

Abstract-This paper presents results from a user evaluation of a robot bartender system, which supports social engagement and interaction with multiple customers. The system is a Nao-based alternative version of an existing robot bartender developed in the JAMES project [1]. The Nao-based version has given us a local experimentation platform, allowing us to focus on social multi-user interaction rather than the robot technology of object manipulation. We will describe the design of the Nao-based system and discuss the differences with the original JAMES system. In a recent evaluation of the JAMES system with real users, a trained and a hand-coded version of the action selection policy were compared [2]. Here we present results from a similar comparative user evaluation on the Nao-based system, which confirm the conclusions of the previous experiment and provide further evidence in favour of the trained action selection mechanism. Task success was found to be almost 20% higher with the trained policy, with interaction times being about 10% shorter. Participants also rated the trained system as significantly more natural, more understanding, and better at providing appropriate attention.

“…The interaction manager then selects high-level actions for the robot, which are processed by the output planner for execution as concrete actions by the talking-head controller and the robot motion planner. Full technical details of the system can be found in [19,25]. The work presented in this paper takes place largely in the context of the state manager (SM), whose primary role is to turn the continuous stream of sensor messages produced by the low-level input-processing components into a discrete representation of the world, the robot, and all entities in the scene, integrating social, interaction-based, and task-based properties.…”

Section: Social Signal Processing In the James Robot Bartendermentioning

confidence: 99%

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

Gaschler

Giuliani

2017

Int J of Soc Robotics

Self Cite

A robot agent designed to engage in real-world human-robot joint action must be able to understand the social states of the human users it interacts with in order to behave appropriately. In particular, in a dynamic public space, a crucial task for the robot is to determine the needs and intentions of all of the people in the scene, so that it only interacts with people who intend to interact with it. We address the task of estimating the engagement state of customers for a robot bartender based on the data from audiovisual sensors. We begin with an offline experiment using hidden Markov models, confirming that the sensor data contains the information necessary to estimate user state. We then present two strategies for online state estimation: a rule-based classifier based on observed human behaviour in real bars, and a set of supervised classifiers trained on a labelled corpus. These strategies are compared in offline cross-validation, in an online user study, and through validation against a separate test corpus. These studies show that while the trained classifiers are best in a cross-validation setting, the rule-based classifier performs best with novel data; however, all classiThis article integrates and extends the work described in the following conference papers: [17,20,23,24 Bristol Robotics Laboratory, University of the West of England, Bristol, UK fiers also change their estimate too frequently for practical use. To address this issue, we present a final classifier based on Conditional Random Fields: this model has comparable performance on the test data, with increased stability. In summary, though, the rule-based classifier shows competitive performance with the trained classifiers, suggesting that for this task, such a simple model could actually be a preferred option, providing useful online performance while avoiding the implementation and data-scarcity issues involved in using machine learning for this task.