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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Autonomous feeding is challenging because it requires manipulation of fooditems with various compliance, sizes, and shapes. To understand how humansmanipulate food items during feeding and to explore ways to adapt theirstrategies to robots, we collected a rich dataset of human trajectories byasking them to pick up food and feed it to a mannequin. From the analysis ofthe collected haptic and motion signals, we demonstrate that humans adapt theircontrol policies to accommodate to the compliance and shape of the food itembeing acquired. We propose a taxonomy of manipulation strategies for feeding tohighlight such policies. As a first step to generate compliance-dependentpolicies, we propose a set of classifiers for compliance-based foodcategorization from haptic and motion signals. We compare these humanmanipulation strategies with fixed position-control policies via a robot. Ouranalysis of success and failure cases of human and robot policies furtherhighlights the importance of adapting the policy to the compliance of a fooditem.

Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulation