Towards Map-Agnostic Policies for Adaptive Informative Path Planning
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arXiv
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| Format: | Preprint |
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2024
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| author | Rückin, Julius Morilla-Cabello, David Stachniss, Cyrill Montijano, Eduardo Popović, Marija |
| author_facet | Rückin, Julius Morilla-Cabello, David Stachniss, Cyrill Montijano, Eduardo Popović, Marija |
| contents | Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is adaptively replanning paths online as the terrain is explored given limited onboard compute resources. Recently, learning-based approaches emerged that train planning policies offline and enable computationally efficient online replanning performing policy inference. These approaches are designed and trained for terrain monitoring missions assuming a single specific map representation, which limits their applicability to different terrains. To address these issues, we propose a novel formulation of the adaptive informative path planning problem unified across different map representations, enabling training and deploying planning policies in a larger variety of monitoring missions. Experimental results validate that our novel formulation easily integrates with classical non-learning-based planning approaches while maintaining their performance. Our trained planning policy performs similarly to state-of-the-art map-specifically trained policies. We validate our learned policy on unseen real-world terrain datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Map-Agnostic Policies for Adaptive Informative Path Planning Rückin, Julius Morilla-Cabello, David Stachniss, Cyrill Montijano, Eduardo Popović, Marija Robotics Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is adaptively replanning paths online as the terrain is explored given limited onboard compute resources. Recently, learning-based approaches emerged that train planning policies offline and enable computationally efficient online replanning performing policy inference. These approaches are designed and trained for terrain monitoring missions assuming a single specific map representation, which limits their applicability to different terrains. To address these issues, we propose a novel formulation of the adaptive informative path planning problem unified across different map representations, enabling training and deploying planning policies in a larger variety of monitoring missions. Experimental results validate that our novel formulation easily integrates with classical non-learning-based planning approaches while maintaining their performance. Our trained planning policy performs similarly to state-of-the-art map-specifically trained policies. We validate our learned policy on unseen real-world terrain datasets. |
| title | Towards Map-Agnostic Policies for Adaptive Informative Path Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.17166 |