Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866909130019569664 |
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| author | Arnob, Raihan Islam Stein, Gregory J. |
| author_facet | Arnob, Raihan Islam Stein, Gregory J. |
| contents | We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide information about the goodness of a particular direction of travel. Building on recent work in learning-augmented model-based planning under uncertainty, we present an approach that can both rely on non-local information to make predictions (via a graph neural network) and is reliable by design: it will always reach its goal, even when learning does not provide accurate predictions. We conduct experiments in three simulated environments in which non-local information is needed to perform well. In our large scale university building environment, generated from real-world floorplans to the scale, we demonstrate a 9.3\% reduction in cost-to-go compared to a non-learned baseline and a 14.9\% reduction compared to a learning-informed planner that can only use local information to inform its predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14501 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information Arnob, Raihan Islam Stein, Gregory J. Robotics Artificial Intelligence We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide information about the goodness of a particular direction of travel. Building on recent work in learning-augmented model-based planning under uncertainty, we present an approach that can both rely on non-local information to make predictions (via a graph neural network) and is reliable by design: it will always reach its goal, even when learning does not provide accurate predictions. We conduct experiments in three simulated environments in which non-local information is needed to perform well. In our large scale university building environment, generated from real-world floorplans to the scale, we demonstrate a 9.3\% reduction in cost-to-go compared to a non-learned baseline and a 14.9\% reduction compared to a learning-informed planner that can only use local information to inform its predictions. |
| title | Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2307.14501 |