NL-SLAM for OC-VLN: Natural Language Grounded SLAM for Object-Centric VLN

• URL: http://arxiv.org/abs/2411.07848v1
• Date: Tue, 12 Nov 2024 15:01:40 GMT
• Title: NL-SLAM for OC-VLN: Natural Language Grounded SLAM for Object-Centric VLN
• Authors: Sonia Raychaudhuri, Duy Ta, Katrina Ashton, Angel X. Chang, Jiuguang Wang, Bernadette Bucher,
• Abstract summary: We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natural language navigation instructions. We also propose Natural Language grounded SLAM (NL-SLAM), a method to ground natural language instruction to robot observations and poses.
• Score: 8.788856156414026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Landmark-based navigation (e.g. go to the wooden desk) and relative positional navigation (e.g. move 5 meters forward) are distinct navigation challenges solved very differently in existing robotics navigation methodology. We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natural language navigation instructions in a method for performing landmark-based navigation. We also propose Natural Language grounded SLAM (NL-SLAM), a method to ground natural language instruction to robot observations and poses. We actively perform NL-SLAM in order to follow object-centric natural language navigation instructions. Our methods leverage pre-trained vision and language foundation models and require no task-specific training. We construct two strong baselines from state-of-the-art methods on related tasks, Object Goal Navigation and Vision Language Navigation, and we show that our approach, NL-SLAM, outperforms these baselines across all our metrics of success on OC-VLN. Finally, we successfully demonstrate the effectiveness of NL-SLAM for performing navigation instruction following in the real world on a Boston Dynamics Spot robot.

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