Conversational Bootstrapping and Other Tricks of a Concierge Robot

Guo, Shang; Lenchner, Jonathan; Connell, Jonathan H.; Dholakia, Mishal; Muta, Hidemasa

doi:10.1145/2909824.3020232

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…Guo et al [37] have performed a study most similar to ours. They built a learning QA system on a Pepper robot using IBM Watson Natural Language Classifier, trained to the specific context of a robot concierge within their institution's "ThinkLab".…”

Section: Related Workmentioning

confidence: 72%

An in-the-wild study to find type of questions people ask to a social robot providing question-answering service

Raza

Vitale

Tonkin

et al. 2022

Intel Serv Robotics

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The role of a human assistant, such as receptionist, is to provide specific information to the public. Questions asked by the public are often context dependent and related to the environment where the assistant is situated. Should similar behaviour and questions be expected when a social robot offers the same assistant service to visitors? Would it be sufficient for the robot to answer only service-specific questions, or is it necessary to design the robot to answer more general questions? This paper aims to answer these research questions by investigating the question-asking behaviour of the public when interacting with a question-answering social robot. We conducted the study at a university event that was open to the public. Results demonstrate that almost no participants asked context-specific questions to the robot. Rather, unrelated questions were common and included queries about the robot’s personal preferences, opinions, thoughts and emotional state. This finding contradicts popular belief and common sense expectations from what is otherwise observed during similar human–human interactions. In addition, we found that incorporating non-context-specific questions in a robot’s database increases the success rate of its question-answering system.

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Section: Related Workmentioning

confidence: 72%

An in-the-wild study to find type of questions people ask to a social robot providing question-answering service

Raza

Vitale

Tonkin

et al. 2022

Intel Serv Robotics

View full text Add to dashboard Cite

show abstract

“…Another consideration for our remote, novice programming proposal is whether this work can be crowdsourced effectively. The success of crowdsourcing in other tasks has been shown extensively in prior work, including for spoken dialog generation for conversational systems (Lasecki et al, 2013;Mitchell et al, 2014;Leite et al, 2016;Yu et al, 2016;Guo et al, 2017;Kennedy et al, 2017;Huang et al, 2018;Jonell et al, 2019) and interaction data and non-verbal behavior (Orkin and Roy, 2007;Orkin and Roy, 2009;Chernova et al, 2010;Rossen and Lok, 2012;Breazeal et al, 2013;Sung et al, 2016). In previous research that is, more relevant to ours, Lee and Ko (2011) crowdsourced non-expert programmers for an online study and found that personified feedback of a robot blaming itself for errors increased the non-programmers' motivation to program.…”

Section: Introductionmentioning

confidence: 95%

“…Prior work has used crowdsourcing for spoken dialog generation for conversational systems ( Jurčíček et al, 2011 ; Lasecki et al, 2013 ; Mitchell et al, 2014 ; Leite et al, 2016 ; Yu et al, 2016 ; Guo et al, 2017 ; Kennedy et al, 2017 ; Huang et al, 2018 ; Jonell et al, 2019 ), interaction data and non-verbal behavior ( Orkin and Roy, 2007 , Orkin and Roy, 2009 ; Chernova et al, 2010 ; Rossen and Lok, 2012 ; Breazeal et al, 2013 ; Sung et al, 2016 ). However, no previous work created a method to collect new robot behaviors for day-to-day tasks on a large scale using semi-situated non-experts.…”

Section: Related Workmentioning

confidence: 99%

Exploring Non-Expert Robot Programming Through Crowdsourcing

et al. 2021

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A longstanding barrier to deploying robots in the real world is the ongoing need to author robot behavior. Remote data collection–particularly crowdsourcing—is increasingly receiving interest. In this paper, we make the argument to scale robot programming to the crowd and present an initial investigation of the feasibility of this proposed method. Using an off-the-shelf visual programming interface, non-experts created simple robot programs for two typical robot tasks (navigation and pick-and-place). Each needed four subtasks with an increasing number of programming statements (if statement, while loop, variables) for successful completion of the programs. Initial findings of an online study (N = 279) indicate that non-experts, after minimal instruction, were able to create simple programs using an off-the-shelf visual programming interface. We discuss our findings and identify future avenues for this line of research.

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“…Moreover, they can be proficient in a single task (e.g., a simple autonomous vacuum cleaner) or implement a variety of behaviours (e.g., a complex waiter robot). Given their versatility and broad range of functionalities, service robots can be deployed in numerous environments, such as libraries, museums [101], hotels [38], cafes [71], hospitals [92], nursing homes [72], and private houses [37].…”

Section: Types Of Healthcare Robotsmentioning

confidence: 99%

Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

Bardaro

Antonini

Motta

2021

Int J of Soc Robotics

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Over the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

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Conversational Bootstrapping and Other Tricks of a Concierge Robot

Cited by 20 publications

References 13 publications

An in-the-wild study to find type of questions people ask to a social robot providing question-answering service

An in-the-wild study to find type of questions people ask to a social robot providing question-answering service

Exploring Non-Expert Robot Programming Through Crowdsourcing

Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

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