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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12048 |
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| _version_ | 1866929218420473856 |
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| author | Kuzma, Volodymyr Humennyy, Vladyslav Partsey, Ruslan |
| author_facet | Kuzma, Volodymyr Humennyy, Vladyslav Partsey, Ruslan |
| contents | We report an improvements to NeurIPS 2023 HomeRobot: Open Vocabulary Mobile Manipulation (OVMM) Challenge reinforcement learning baseline. More specifically, we propose more accurate semantic segmentation module, along with better place skill policy, and high-level heuristic that outperforms the baseline by 2.4% of overall success rate (sevenfold improvement) and 8.2% of partial success rate (1.75 times improvement) on Test Standard split of the challenge dataset. With aforementioned enhancements incorporated our agent scored 3rd place in the challenge on both simulation and real-world stages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12048 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HomeRobot Open Vocabulary Mobile Manipulation Challenge 2023 Participant Report (Team KuzHum) Kuzma, Volodymyr Humennyy, Vladyslav Partsey, Ruslan Robotics Computer Vision and Pattern Recognition We report an improvements to NeurIPS 2023 HomeRobot: Open Vocabulary Mobile Manipulation (OVMM) Challenge reinforcement learning baseline. More specifically, we propose more accurate semantic segmentation module, along with better place skill policy, and high-level heuristic that outperforms the baseline by 2.4% of overall success rate (sevenfold improvement) and 8.2% of partial success rate (1.75 times improvement) on Test Standard split of the challenge dataset. With aforementioned enhancements incorporated our agent scored 3rd place in the challenge on both simulation and real-world stages. |
| title | HomeRobot Open Vocabulary Mobile Manipulation Challenge 2023 Participant Report (Team KuzHum) |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.12048 |