We Asked 100 People: How Would You Train Our Robot?

Johannes Pfau, Rainer Malaka

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

While robotic proficiency excels in constrained environments, the demand for vast amounts of world knowledge to cover unforeseen circumstances, constellations and tasks prevents sufficiently robust real-world application. Human computation has shown to provide successful advances to close this reasoning gap and accumulate knowledge, yet being greatly reliant on the quality of the provided data. In this paper, we introduce the game with a purpose Tool Feud that collects popularity rankings of object choices for robotic everyday activity tasks and evaluate an approach for classifying malicious responses automatically.
Original languageEnglish
Title of host publicationExtended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play
PublisherAssociation for Computing Machinery
Pages335-339
Number of pages5
DOIs
Publication statusPublished - 2020
Externally publishedYes

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