English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To assist human users according to their individual preference in assemblytasks, robots typically require user demonstrations in the given task. However,providing demonstrations in actual assembly tasks can be tedious andtime-consuming. Our thesis is that we can learn user preferences in assemblytasks from demonstrations in a representative canonical task. Inspired byprevious work in economy of human movement, we propose to represent userpreferences as a linear function of abstract task-agnostic features, such asmovement and physical and mental effort required by the user. For each user, welearn their preference from demonstrations in a canonical task and use thelearned preference to anticipate their actions in the actual assembly taskwithout any user demonstrations in the actual task. We evaluate our proposedmethod in a model-airplane assembly study and show that preferences can beeffectively transferred from canonical to actual assembly tasks, enablingrobots to anticipate user actions.

Towards Transferring Human Preferences from Canonical to Actual Assembly Tasks