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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03165 |
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| _version_ | 1866912688575086592 |
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| author | Kathirvel, Raj Surya Rajendran Chavis, Zach A Guy, Stephen J. Desingh, Karthik |
| author_facet | Kathirvel, Raj Surya Rajendran Chavis, Zach A Guy, Stephen J. Desingh, Karthik |
| contents | We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environment in a JSON text format, we enable semantic information to be added and edited in a format that both humans and FMs understand, while grounding the robot to existing nodes during planning to avoid infeasible states during deployment. Our proposed framework employs a two stage approach, first mapping the environment alongside an operator with a Vision-FM, then using the SENT-Map representation alongside a natural-language query within an FM for planning. Our experimental results show that semantic-enhancement enables even small locally-deployable FMs to successfully plan over indoor environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SENT Map -- Semantically Enhanced Topological Maps with Foundation Models Kathirvel, Raj Surya Rajendran Chavis, Zach A Guy, Stephen J. Desingh, Karthik Robotics We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environment in a JSON text format, we enable semantic information to be added and edited in a format that both humans and FMs understand, while grounding the robot to existing nodes during planning to avoid infeasible states during deployment. Our proposed framework employs a two stage approach, first mapping the environment alongside an operator with a Vision-FM, then using the SENT-Map representation alongside a natural-language query within an FM for planning. Our experimental results show that semantic-enhancement enables even small locally-deployable FMs to successfully plan over indoor environments. |
| title | SENT Map -- Semantically Enhanced Topological Maps with Foundation Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.03165 |