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

It is critical for advanced manufacturing machines to autonomously execute atask by following an end-user's natural language (NL) instructions. However, NLinstructions are usually ambiguous and abstract so that the machines maymisunderstand and incorrectly execute the task. To address this NL-basedhuman-machine communication problem and enable the machines to appropriatelyexecute tasks by following the end-user's NL instructions, we developed aMachine-Executable-Plan-Generation (exePlan) method. The exePlan methodconducts task-centered semantic analysis to extract task-related informationfrom ambiguous NL instructions. In addition, the method specifies machineexecution parameters to generate a machine-executable plan by interpretingabstract NL instructions. To evaluate the exePlan method, an industrial robotBaxter was instructed by NL to perform three types of industrial tasks {'drilla hole', 'clean a spot', 'install a screw'}. The experiment results proved thatthe exePlan method was effective in generating machine-executable plans fromthe end-user's NL instructions. Such a method has the promise to endow amachine with the ability of NL-instructed task execution.

Generating machine-executable plans from end-user's natural-language instructions