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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.14154 |
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| _version_ | 1866910732057051136 |
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| author | Liu, Yaotian Cao, Yu Zhang, Jeff |
| author_facet | Liu, Yaotian Cao, Yu Zhang, Jeff |
| contents | In ObjectNav, agents must locate specific objects within unseen environments, requiring effective perception, prediction, localization and planning capabilities. This study finds that state-of-the-art embodied AI agents compete for higher navigation quality, but often compromise the computational efficiency. To address this issue, we introduce "Skip-SCAR," an optimization framework that builds computationally and memory-efficient embodied AI agents to accomplish high-quality visual navigation tasks. Skip-SCAR opportunistically skips the redundant step computations during semantic segmentation and local re-planning without hurting the navigation quality. Skip-SCAR also adopts a novel hybrid sparse and dense network for object prediction, optimizing both the computation and memory footprint. Tested on the HM3D ObjectNav datasets and real-world physical hardware systems, Skip-SCAR not only minimizes hardware resources but also sets new performance benchmarks, demonstrating the benefits of optimizing both navigation quality and computational efficiency for robotics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14154 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skip-SCAR: Hardware-Friendly High-Quality Embodied Visual Navigation Liu, Yaotian Cao, Yu Zhang, Jeff Robotics In ObjectNav, agents must locate specific objects within unseen environments, requiring effective perception, prediction, localization and planning capabilities. This study finds that state-of-the-art embodied AI agents compete for higher navigation quality, but often compromise the computational efficiency. To address this issue, we introduce "Skip-SCAR," an optimization framework that builds computationally and memory-efficient embodied AI agents to accomplish high-quality visual navigation tasks. Skip-SCAR opportunistically skips the redundant step computations during semantic segmentation and local re-planning without hurting the navigation quality. Skip-SCAR also adopts a novel hybrid sparse and dense network for object prediction, optimizing both the computation and memory footprint. Tested on the HM3D ObjectNav datasets and real-world physical hardware systems, Skip-SCAR not only minimizes hardware resources but also sets new performance benchmarks, demonstrating the benefits of optimizing both navigation quality and computational efficiency for robotics. |
| title | Skip-SCAR: Hardware-Friendly High-Quality Embodied Visual Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.14154 |