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

• URL: http://arxiv.org/abs/2403.18222v2
• Date: Sun, 28 Jul 2024 14:21:52 GMT
• Title: Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
• Authors: Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai Matni,
• Abstract summary: 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. We implement our approach in simulation using three such pre-trained models, and showcase its potential to significantly enhance task completion rates.
• Score: 29.00293625794431
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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