  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
