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

In this paper, we present a framework, probabilistic model, and algorithm for learning shared control policies by observing an assistant. This is a methodology we refer to as Learning Assistance by Demonstration (LAD). As a subset of robot Learning by Demonstration (LbD), LAD focuses on the assistive element by explicitly capturing how and when to help. The latter is especially important in assistive scenarios---such as rehabilitation and training---where there exists multiple and possibly conflicting goals. We formalize these notions in a probabilistic model and develop an efficient online mixture of experts (OME) algorithm, based on sparse Gaussian processes (GPs), for learning the assistive policy. Focusing on smart mobility, we couple the LAD methodology with a novel paired-haptic-controllers setup for helping smart wheelchair users navigate their environment. Experimental results with 15 able-bodied participants demonstrate that our learned shared control policy improved driving performance (as measured in lap seconds) by 43 s (a speedup of 191%). Furthermore, survey results indicate that the participants not only performed better quantitatively, but also qualitatively felt the model assistance helped them complete the task.

Learning Assistance by Demonstration: Smart Mobility With Shared Control and Paired Haptic Controllers