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 difficult for humans to efficiently teach robots how to correctlyperform a task. One intuitive solution is for the robot to iteratively learnthe human's preferences from corrections, where the human improves the robot'scurrent behavior at each iteration. When learning from corrections, we arguethat while the robot should estimate the most likely human preferences, itshould also know what it does not know, and integrate this uncertainty as itmakes decisions. We advance the state-of-the-art by introducing a Kalman filterfor learning from corrections: this approach obtains the uncertainty of theestimated human preferences. Next, we demonstrate how the estimate uncertaintycan be leveraged for active learning and risk-sensitive deployment. Our resultsindicate that obtaining and leveraging uncertainty leads to faster learningfrom human corrections.

Including Uncertainty when Learning from Human Corrections