Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
<|reference_start|>Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies: 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<|reference_end|>
@article{wu2024uncertainty-aware,
    title={Uncertainty-Aware Deployment of Pre-trained Language-Conditioned
  Imitation Learning Policies},
    author={Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, Nikolai
  Matni},
    journal={arXiv preprint arXiv:2403.18222},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18222},
    primaryClass={cs.RO cs.LG}
}