{"kagg_id":"KAGG:2607.00007","slug":"2607.00007","title":"Multi-Modal Range Fusion for Agile Quadruped Navigation in Dense Vegetation","authors":[{"name":"Farah Idris","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"},{"name":"Hao Chen","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"},{"name":"Kai Wen Lim","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"}],"affiliation":null,"abstract":"Quadruped robots are expected to move quickly through forests, parks, and overgrown terrain, yet LiDAR range sensing degrades precisely there: foliage produces premature returns, thin stems and branches fall between beams, and occlusion hides the rigid structure that constrains motion. This paper presents a range-fusion method that combines LiDAR and stereo-vision range estimates with weights conditioned on where the robot is predicted to move. A configuration-driven local planner, in the sense of AgniNav, supplies the robot's collision envelope and candidate trajectories; a first-order model of velocity tracking converts them into a motion-predicted occupancy field over the sensing grid. Per-cell fusion weights combine this field with modality-specific noise, motion-induced latency error, and a vegetation degradation model whose density parameter is estimated online from LiDAR transmittance. The method is evaluated on a Unitree Go2 carrying a stereo rig and a 32-beam LiDAR, in an Isaac Lab synthetic forest suite with four vegetation density classes and on a 600 m park course through shrub and sapling cover. Compared with LiDAR-only sensing, it reduces range estimate error from 0.99 m to 0.94 m, raises mean speed from 2.07 m/s to 2.31 m/s, and raises obstacle-free success from 84.6 to 90.7 percent, while mean obstacle clearance decreases from 18.1 cm to 16.8 cm. Uniform-weight fusion reaches only 84.9 percent success, and removing any single component lowers success. A failure taxonomy covers dense sapling cover and head-height branches.","publication_date":"2026-07-24","registered_at":"2026-07-25T09:45:00Z","updated_at":"2026-07-25T17:02:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00007/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, 2019. arXiv:1901.08652.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, 2022. arXiv:2201.08117.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. Field Robotics, 2022. arXiv:2201.07067.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Bouman et al.. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. arXiv:2010.09259.","identifier":"arXiv:2010.09259","url":"https://arxiv.org/abs/2010.09259"},{"text":"H. Hirschmüller. Stereo Processing by Semiglobal Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):328-341, 2008. doi:10.1109/TPAMI.2007.1166.","identifier":"doi:10.1109/TPAMI.2007.1166","url":"https://doi.org/10.1109/TPAMI.2007.1166"},{"text":"J.-R. Chang and Y.-S. Chen. Pyramid Stereo Matching Network. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. arXiv:1803.08669.","identifier":"arXiv:1803.08669","url":"https://arxiv.org/abs/1803.08669"},{"text":"L. Lipson, Z. Teed, and J. Deng. RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. International Conference on 3D Vision (3DV), 2021. arXiv:2109.07547.","identifier":"arXiv:2109.07547","url":"https://arxiv.org/abs/2109.07547"},{"text":"T. Zang, S. Cheng, H. Huang, S. Wang, and W. Zhang. AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation. arXiv:2606.10903, 2026.","identifier":"arXiv:2606.10903","url":"https://arxiv.org/abs/2606.10903"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. Conference on Robot Learning (CoRL), 2021. arXiv:2109.11978.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems (RSS), 2021. arXiv:2107.04034.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. Conference on Robot Learning (CoRL), 2022. arXiv:2211.07638.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"text":"R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang. Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. International Conference on Learning Representations (ICLR), 2022. arXiv:2107.03996.","identifier":"arXiv:2107.03996","url":"https://arxiv.org/abs/2107.03996"},{"text":"C. S. Imai et al.. Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2109.14549.","identifier":"arXiv:2109.14549","url":"https://arxiv.org/abs/2109.14549"},{"text":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. Conference on Robot Learning (CoRL), 2023. arXiv:2309.05665.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023.","identifier":"arXiv:2309.14341","url":"https://arxiv.org/abs/2309.14341"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2209.12827.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, 2021. arXiv:2103.04351.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"text":"Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. arXiv:2112.02094.","identifier":"arXiv:2112.02094","url":"https://arxiv.org/abs/2112.02094"},{"text":"T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2204.12876.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics and Automation Letters, 2021. arXiv:2002.05700.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"M. V. Gasparino et al.. WayFAST: Navigation with Predictive Traversability in the Field. IEEE Robotics and Automation Letters, 2022. arXiv:2203.12071.","identifier":"arXiv:2203.12071","url":"https://arxiv.org/abs/2203.12071"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems (RSS), 2023. arXiv:2305.08510.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"M. Mattamala et al.. Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision. arXiv:2404.07110, 2024.","identifier":"arXiv:2404.07110","url":"https://arxiv.org/abs/2404.07110"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. Science Robotics, 2021. arXiv:2110.05113.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"H. Xi, M. Tan, X. Zhang, S. Cheng, S. Wang, Y. Gu, X. Shen, and W. Zhang. CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation. arXiv:2603.19602, 2026.","identifier":"arXiv:2603.19602","url":"https://arxiv.org/abs/2603.19602"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. IEEE International Conference on Robotics and Automation (ICRA), 2023. arXiv:2210.03370.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23-33, 1997. doi:10.1109/100.580977.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen. Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight. IEEE Robotics and Automation Letters, 2019. arXiv:1907.01531.","identifier":"arXiv:1907.01531","url":"https://arxiv.org/abs/1907.01531"},{"text":"X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao. EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors. IEEE Robotics and Automation Letters, 2021. arXiv:2008.08835.","identifier":"arXiv:2008.08835","url":"https://arxiv.org/abs/2008.08835"},{"text":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. Autonomous Robots, 2022. arXiv:2011.13112.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"text":"A. Elfes. Using Occupancy Grids for Mobile Robot Perception and Navigation. Computer, 22(6):46-57, 1989. doi:10.1109/2.30720.","identifier":"doi:10.1109/2.30720","url":"https://doi.org/10.1109/2.30720"},{"text":"H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. 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Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation for Robot Learning. NeurIPS Datasets and Benchmarks Track, 2021. arXiv:2108.10470.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics and Automation Letters, 2023. arXiv:2301.04195.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. arXiv:1703.06907.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00007","metadata":{"title":"Multi-Modal Range Fusion for Agile Quadruped Navigation in Dense Vegetation","authors":[{"name":"Farah Idris","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"},{"name":"Hao Chen","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"},{"name":"Kai Wen Lim","affiliation":"Robotics and Control Laboratory, Nanyang Technological University, Singapore"}],"publication_date":"2026-07-24","abstract":"Quadruped robots are expected to move quickly through forests, parks, and overgrown terrain, yet LiDAR range sensing degrades precisely there: foliage produces premature returns, thin stems and branches fall between beams, and occlusion hides the rigid structure that constrains motion. This paper presents a range-fusion method that combines LiDAR and stereo-vision range estimates with weights conditioned on where the robot is predicted to move. A configuration-driven local planner, in the sense of AgniNav, supplies the robot's collision envelope and candidate trajectories; a first-order model of velocity tracking converts them into a motion-predicted occupancy field over the sensing grid. Per-cell fusion weights combine this field with modality-specific noise, motion-induced latency error, and a vegetation degradation model whose density parameter is estimated online from LiDAR transmittance. The method is evaluated on a Unitree Go2 carrying a stereo rig and a 32-beam LiDAR, in an Isaac Lab synthetic forest suite with four vegetation density classes and on a 600 m park course through shrub and sapling cover. Compared with LiDAR-only sensing, it reduces range estimate error from 0.99 m to 0.94 m, raises mean speed from 2.07 m/s to 2.31 m/s, and raises obstacle-free success from 84.6 to 90.7 percent, while mean obstacle clearance decreases from 18.1 cm to 16.8 cm. Uniform-weight fusion reaches only 84.9 percent success, and removing any single component lowers success. A failure taxonomy covers dense sapling cover and head-height branches.","references":[{"text":"J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, 2019. arXiv:1901.08652.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, 2022. arXiv:2201.08117.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. Field Robotics, 2022. arXiv:2201.07067.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Bouman et al.. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020. arXiv:2010.09259.","identifier":"arXiv:2010.09259","url":"https://arxiv.org/abs/2010.09259"},{"text":"H. Hirschmüller. Stereo Processing by Semiglobal Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2):328-341, 2008. doi:10.1109/TPAMI.2007.1166.","identifier":"doi:10.1109/TPAMI.2007.1166","url":"https://doi.org/10.1109/TPAMI.2007.1166"},{"text":"J.-R. Chang and Y.-S. Chen. Pyramid Stereo Matching Network. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. arXiv:1803.08669.","identifier":"arXiv:1803.08669","url":"https://arxiv.org/abs/1803.08669"},{"text":"L. Lipson, Z. Teed, and J. Deng. RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. International Conference on 3D Vision (3DV), 2021. arXiv:2109.07547.","identifier":"arXiv:2109.07547","url":"https://arxiv.org/abs/2109.07547"},{"text":"T. Zang, S. Cheng, H. Huang, S. Wang, and W. Zhang. AgniNav: Configuration-Driven Cross-Embodiment Local Planning for Robot Navigation. arXiv:2606.10903, 2026.","identifier":"arXiv:2606.10903","url":"https://arxiv.org/abs/2606.10903"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. Conference on Robot Learning (CoRL), 2021. arXiv:2109.11978.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems (RSS), 2021. arXiv:2107.04034.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. Conference on Robot Learning (CoRL), 2022. arXiv:2211.07638.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"text":"R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang. Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. International Conference on Learning Representations (ICLR), 2022. arXiv:2107.03996.","identifier":"arXiv:2107.03996","url":"https://arxiv.org/abs/2107.03996"},{"text":"C. S. Imai et al.. Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2109.14549.","identifier":"arXiv:2109.14549","url":"https://arxiv.org/abs/2109.14549"},{"text":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. Conference on Robot Learning (CoRL), 2023. arXiv:2309.05665.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023.","identifier":"arXiv:2309.14341","url":"https://arxiv.org/abs/2309.14341"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2209.12827.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, 2021. arXiv:2103.04351.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"text":"Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. arXiv:2112.02094.","identifier":"arXiv:2112.02094","url":"https://arxiv.org/abs/2112.02094"},{"text":"T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2204.12876.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics and Automation Letters, 2021. arXiv:2002.05700.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"M. V. Gasparino et al.. WayFAST: Navigation with Predictive Traversability in the Field. IEEE Robotics and Automation Letters, 2022. arXiv:2203.12071.","identifier":"arXiv:2203.12071","url":"https://arxiv.org/abs/2203.12071"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems (RSS), 2023. arXiv:2305.08510.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"M. Mattamala et al.. Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision. arXiv:2404.07110, 2024.","identifier":"arXiv:2404.07110","url":"https://arxiv.org/abs/2404.07110"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. Science Robotics, 2021. arXiv:2110.05113.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"H. Xi, M. Tan, X. Zhang, S. Cheng, S. Wang, Y. Gu, X. Shen, and W. Zhang. CeRLP: A Cross-embodiment Robot Local Planning Framework for Visual Navigation. arXiv:2603.19602, 2026.","identifier":"arXiv:2603.19602","url":"https://arxiv.org/abs/2603.19602"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. IEEE International Conference on Robotics and Automation (ICRA), 2023. arXiv:2210.03370.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23-33, 1997. doi:10.1109/100.580977.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen. Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight. 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