Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916597641248768 |
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| author | Arnob, Raihan Islam Stein, Gregory J. |
| author_facet | Arnob, Raihan Islam Stein, Gregory J. |
| contents | We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of unseen space, data used to train a graph neural network to predict the goodness of temporally-extended exploratory actions. Our learning-augmented model-based planning approach predicts the expected value of information of revealing unseen space and is capable of using these predictions to actively seek information and so improve long-horizon navigation. Across two simulated office-like environments, our planner outperforms competitive learned and non-learned baseline navigation strategies, achieving improvements of up to 63.76% and 36.68%, demonstrating its capacity to actively seek performance-critical information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03269 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information Arnob, Raihan Islam Stein, Gregory J. Robotics We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of unseen space, data used to train a graph neural network to predict the goodness of temporally-extended exploratory actions. Our learning-augmented model-based planning approach predicts the expected value of information of revealing unseen space and is capable of using these predictions to actively seek information and so improve long-horizon navigation. Across two simulated office-like environments, our planner outperforms competitive learned and non-learned baseline navigation strategies, achieving improvements of up to 63.76% and 36.68%, demonstrating its capacity to actively seek performance-critical information. |
| title | Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.03269 |