Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies

Bo Wu; Bruce D. Lee; Kostas Daniilidis; Bernadette Bucher; Nikolai Matni

IROS 2024poster1 citations

Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies

Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai Matni

Abstract

Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge. Toward addressing this challenge, we propose a novel approach for uncertainty-aware deployment of pre-trained language-conditioned imitation learning agents. Specifically, we use temperature scaling to calibrate these models and exploit the calibrated model to make uncertainty-aware decisions by aggregating the local information of candidate actions. We implement our approach in simulation using three such pre-trained models, and showcase its potential to significantly enhance task completion rates. The accompanying code is accessible at the link: https://github.com/BobWu1998/uncertainty_quant_all.git

BibTeX

@inproceedings{iros2024_uncertaintyaware,
  title = {Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies},
  author = {Bo Wu and Bruce D. Lee and Kostas Daniilidis and Bernadette Bucher and Nikolai Matni},
  booktitle = {IROS 2024},
  year = {2024}
}