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Communication Dans Un Congrès Année : 2020

The Unexpected Daily Situations (UDS) Dataset

Yoan Sallami
Séverin Lemaignan
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Rachid Alami

Résumé

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 modeling. 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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Dates et versions

hal-02927549 , version 1 (01-09-2020)

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Citer

Yoan Sallami, Katie Winkle, Nicola Webb, Séverin Lemaignan, Rachid Alami. The Unexpected Daily Situations (UDS) Dataset. ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), Mar 2020, Cambridge, United Kingdom. pp.427-429, ⟨10.1145/3371382.3378270⟩. ⟨hal-02927549⟩
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