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

arXiv:2403.18222v1 Announce Type: cross
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 …

arxiv cs.lg cs.ro deployment imitation learning language policies type uncertainty