The Unexpected Daily Situations (UDS) Dataset: A New Benchmark for Socially-Aware Assistive Robots
Authors: 
Yoan Sallami, Katie Winkle, Nicola Webb, Severin Lemaignan, Rachid AlamiAuthors Info & Claims
Yoan Sallami, Katie Winkle, Nicola Webb, Severin Lemaignan, Rachid AlamiAuthors Info & ClaimsPages 427 - 429
Abstract
This article presents the progress in building a new dataset of 'unexpected daily situations' (like someone tripping on a box, while carrying a tray to the kitchen, or someone burning him/herself with hot water and dropping a mug). Each of the situations involve one or two humans in a familiar, structured environment (eg, a kitchen, a living room) with rich semantics. Correctly interpreting the situation (including recognising an error, undesired effect or incongruity when it occurs, as well as selecting the best repair action) requires beyond-state-of-art spatio-temporal, semantic and socio-cognitive modelling. As such, the aim of the dataset is to offer (i) a realistic source of data to train and test such novel algorithms and (ii) provide a new benchmark against which algorithms can be demonstrated.
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- The Unexpected Daily Situations (UDS) Dataset: A New Benchmark for Socially-Aware Assistive Robots
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Information & Contributors
Information
Published In
March 2020
702 pages
ISBN:9781450370578
DOI:10.1145/3371382
- General Chairs:
- Tony Belpaeme,
- James Young,
- Program Chairs:
- Hatice Gunes,
- Laurel Riek
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Association for Computing Machinery
New York, NY, United States
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Published: 01 April 2020
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HRI '20: ACM/IEEE International Conference on Human-Robot Interaction
March 23 - 26, 2020
Cambridge, United Kingdom
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Overall Acceptance Rate 192 of 519 submissions, 37%
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