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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.12723 |
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| _version_ | 1866912640125632512 |
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| author | Zhang, Kai Lucet, Eric Sandretto, Julien Alexandre Dit Chen, Shoubin Filliat, David |
| author_facet | Zhang, Kai Lucet, Eric Sandretto, Julien Alexandre Dit Chen, Shoubin Filliat, David |
| contents | Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the corresponding time cost intervals for removing and bypassing obstacles, optimizing both the success rate and time efficiency, ensuring safer and more efficient navigation. We validate our method through extensive simulations and real-world experiments, demonstrating significant improvements over existing NAMO frameworks. More details can be found in our website: https://kai-zhang-er.github.io/namo-uncertainty/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval Zhang, Kai Lucet, Eric Sandretto, Julien Alexandre Dit Chen, Shoubin Filliat, David Robotics Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the corresponding time cost intervals for removing and bypassing obstacles, optimizing both the success rate and time efficiency, ensuring safer and more efficient navigation. We validate our method through extensive simulations and real-world experiments, demonstrating significant improvements over existing NAMO frameworks. More details can be found in our website: https://kai-zhang-er.github.io/namo-uncertainty/ |
| title | NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.12723 |