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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Contact-rich manipulation tasks with stiff frictional elements like connectorinsertion are difficult to model with rigid-body simulators. In this work, wepropose a new approach for modeling these environments by learning aquasi-static contact force model instead of a full simulator. Using a featurevector that contains information about the configuration and control, we find alinear mapping adequately captures the relationship between this feature vectorand the sensed contact forces. A novel Linear Model Learning (LML) algorithm isused to solve for the globally optimal mapping in real time without any matrixinversions, resulting in an algorithm that runs in nearly constant time on aGPU as the model size increases. We validate the proposed approach forconnector insertion both in simulation and hardware experiments, where thelearned model is combined with an optimization-based controller to achievesmooth insertions in the presence of misalignments and uncertainty. Our websitefeaturing videos, code, and more materials is available athttps://model-based-plugging.github.io/.

Efficient Online Learning of Contact Force Models for Connector Insertion