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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.18222 |
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| _version_ | 1866913449993306112 |
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| author | Wu, Bo Lee, Bruce D. Daniilidis, Kostas Bucher, Bernadette Matni, Nikolai |
| author_facet | Wu, Bo Lee, Bruce D. Daniilidis, Kostas Bucher, Bernadette Matni, Nikolai |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies Wu, Bo Lee, Bruce D. Daniilidis, Kostas Bucher, Bernadette Matni, Nikolai Robotics Machine Learning 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 |
| title | Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2403.18222 |