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

Robots learning from observations in the real world using inversereinforcement learning (IRL) may encounter objects or agents in theenvironment, other than the expert, that cause nuisance observations during thedemonstration. These confounding elements are typically removed infully-controlled environments such as virtual simulations or lab settings. Whencomplete removal is impossible the nuisance observations must be filtered out.However, identifying the source of observations when large amounts ofobservations are made is difficult. To address this, we present a hierarchicalBayesian model that incorporates both the expert's and the confoundingelements' observations thereby explicitly modeling the diverse observations arobot may receive. We extend an existing IRL algorithm originally designed towork under partial occlusion of the expert to consider the diverseobservations. In a simulated robotic sorting domain containing both occlusionand confounding elements, we demonstrate the model's effectiveness. Inparticular, our technique outperforms several other comparative methods, secondonly to having perfect knowledge of the subject's trajectory.

A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments