Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction


Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation


Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results


Conclusion and Future Work, Disclosure statement, and References Related Works
2.1. Vision-and-Language Navigation
2.2. Semantic Scene Understanding and Instance Segmentation
2.3. 3D Scene Reconstruction Related Works 2.1. Vision-and-Language Navigation 2.1. Vision-and-Language Navigation 2.2. Semantic Scene Understanding and Instance Segmentation 2.2. Semantic Scene Understanding and Instance Segmentation 2.3. 3D Scene Reconstruction 2.3. 3D Scene Reconstruction Methodology
3.1. Data Collection
3.2. Open-set Semantic Information from Images
3.3. Creating the Open-set 3D Representation
3.4. Language-Guided Navigation Methodology Methodology 3.1. Data Collection 3.1. Data Collection 3.2. Open-set Semantic Information from Images 3.2. Open-set Semantic Information from Images 3.3. Creating the Open-set 3D Representation 3.3. Creating the Open-set 3D Representation 3.4. Language-Guided Navigation 3.4. Language-Guided Navigation Experiments
4.1. Quantitative Evaluation
4.2. Qualitative Results Experiments Experiments 4.1. Quantitative Evaluation 4.1. Quantitative Evaluation 4.2. Qualitative Results 4.2. Qualitative Results Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References Conclusion and Future Work, Disclosure statement, and References 5. Conclusion and Future Work In conclusion, we introduce O3D-SIM, an innovative approach to scene representation that builds on the concepts established in our previous work, SI-Maps. We demonstrated that an instance-level understanding of an environment enhances performance in language-guided tasks. O3D-SIM advances the 2D closed-set approach of SI-Maps to an open-set 3D representation. When combined with a Large Language Model (LLM) to incorporate natural language understanding, O3D-SIM significantly improves the success rate of executing natural language queries. This advancement suggests that representations akin to O3D-SIM could enable robots to approach the comprehension and execution of natural language commands nearly as effectively as humans. A promising avenue for future development involves integrating dynamic objects into O3D-SIM. Although it currently identifies static objects, equipping the pipeline to recognize humans and other dynamic entities would enable handling more complex queries, bringing them closer to human levels of understanding. Such an enhancement would be invaluable in various settings, including hospitals and household robotics, or any context where human-robot collaboration is crucial. Another intriguing avenue for exploration is the integration of 3D instance-level scene representations, such as O3DSIM, with physics engines to transform real-life environments into life-like simulations, akin to Nvidia Isaac Gym [40]. 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[38] Raphael Schumann and Stefan Riezler. Analyzing generalization of vision and language navigation to unseen outdoor areas. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7519–7532, Dublin, Ireland, May 2022. Association for Computational Linguistics. [39] Kanishk Jain, Varun Chhangani, Amogh Tiwari, K. Madhava Krishna, and Vineet Gandhi. Ground then navigate: Language-guided navigation in dynamic scenes. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 4113–4120, 2023. [40] Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al. Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint, 2021. Authors:
(1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India;
(3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work;
(4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India;
(5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey;
(6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. Authors: Authors: (1) Laksh Nanwani, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (2) Kumaraditya Gupta, International Institute of Information Technology, Hyderabad, India; (3) Aditya Mathur, International Institute of Information Technology, Hyderabad, India; this author contributed equally to this work; (4) Swayam Agrawal, International Institute of Information Technology, Hyderabad, India; (5) A.H. Abdul Hafez, Hasan Kalyoncu University, Sahinbey, Gaziantep, Turkey; (6) K. Madhava Krishna, International Institute of Information Technology, Hyderabad, India. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license. available on arxiv available on arxiv

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Advancing VLN: O3D-SIM's Role in Human-Robot Collaboration and Simulation

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