Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
This work addresses the problem of improving robot reliability in varied conditions for robotics applications, but it is incremental as it builds on existing pre-trained models with a calibration technique.
The paper tackles the challenge of reliable generalization for large-scale robotic policies to new environments by proposing an uncertainty-aware deployment method for pre-trained language-conditioned imitation learning agents, resulting in significantly enhanced task completion rates in simulation.
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