Negotiating Instruction Strategies during Robot Action Demonstration

Jensen, Lars Christian; Fischer, Kerstin; Shukla, Dadhichi; Piater, Justus

doi:10.1145/2701973.2702036

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some authors commented on disproportionate counts between men and women participants. For example, Jensen et al [96] wrote about an "uneven distribution of gender," while Karreman et al [97] reported on male-male and male-female pairs, but no femalefemale pairs, on account of being unable to recruit equivalent numbers of female participants. Yet, there was virtually no [91] were reported as being from South Africa, but the location of the study was not explicitly reported difference in the number of men and women within each study overall.…”

Section: Sex and Gendermentioning

confidence: 99%

Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Seaborn

Barbareschi

Chandra

2023

Int J of Soc Robotics

View full text Add to dashboard Cite

Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called “WEIRD”: Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human–computer interaction (HCI) research, as well. How then does human–robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006–2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.

show abstract

Section: Sex and Gendermentioning

confidence: 99%

Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Seaborn

Barbareschi

Chandra

2023

Int J of Soc Robotics

View full text Add to dashboard Cite

show abstract

“…The signals are given by performing and gestures, respectively. The HRI study by Jensen et al ( 2015 ) shows that for an appropriate robot action often no feedback or rarely positive feedback is given. On the other hand, users consistently gave negative feedback for unexpected actions.…”

Section: Human-robot Collaboration Scenariomentioning

confidence: 99%

“…Owing to these reasons, we restrict ourselves to use static hand gestures from the Innsbruck Multi-view Hand Gestures (IMHG) dataset 1 (Shukla et al, 2016 ) to instruct the robot. They were designed based on our previous HRI study (Jensen et al, 2015 ) conducted with participants having no experience in interacting with the robot. They are closely related to the semantic content of verbal language.…”

Section: Introductionmentioning

confidence: 99%

Learning Semantics of Gestural Instructions for Human-Robot Collaboration

2018

Self Cite

View full text Add to dashboard Cite

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.

show abstract