Article

LOPAL: Local Performance-Aware Active Learning from Imperfect Demonstrations

LOPAL is a local performance-aware active learning method that uses a Gaussian Mixture Model (GMM) to jointly encode demonstration trajectories and their local quality assessments, thereby generating trajectories that outperform the imperfect demonstrations. The method also uses a shared autonomy (SA) mechanism to actively request user corrections in areas with insufficient data, while the robot autonomously executes its learned behavior during this process.

Part of the event: LOPAL: Active Learning with Local Quality Assessment for Imperfect Demonstrations

This page provides a summary and source link only.