{"kagg_id":"KAGG:2610.00003","slug":"2610.00003","title":"A Cross-Embodiment Navigation Stack for Heterogeneous Robot Teams: Planner, Interface Calibration, and Future-Range Perception","authors":[{"name":"Zhaoyu Li","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Jingyao Sun","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Yichen Wang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Bowen Huang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Tianyu Zhou","affiliation":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}],"affiliation":null,"abstract":"Running one navigation stack on a heterogeneous robot team requires more than a planner that accepts different footprints: each robot's velocity interface drifts with payload and battery state, and range perception must be queried along the motions that the robot can actually execute. This paper presents a cross-embodiment navigation stack with three coupled layers that share a single data contract keyed on a versioned configuration descriptor. A configuration-driven local planner built on AgniNav plans over abstract robot configurations; an active interface calibration layer built on GAUGE keeps each robot's command-to-motion model current through planner-conditioned probes triggered by residual drift; and a control-aligned future-range layer built on FutureRay supplies free-range predictions along the directions each embodiment can execute under its calibrated interface. The stack was deployed on a mixed team of a Go2-class quadruped, a B2-class quadruped, and a wheeled robot on a three-floor indoor-adjacent course with doorways, a 12 cm step, and a light-degraded stairwell, during a 14 h campaign and short ablation runs. The full stack reached 88.2 percent end-to-end task success with a 28 ms p95 replanning latency. Removing any single layer lowered success by 3.0 to 10.6 percentage points, although the two recorded ablation summaries disagree on which layer matters most. A simulated humanoid embodiment was onboarded in 2.1 h and succeeded in 94 percent of 10 runs. The paper also characterizes the failure envelope of the stack.","publication_date":"2026-10-05","registered_at":"2026-10-06T09:07:00Z","updated_at":"2026-10-06T11:09:00Z","status":"published","html_path":null,"pdf_path":"articles/2610.00003/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"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":"T. Zang, H. Huang, S. Cheng, and W. Zhang. GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces. arXiv:2607.02841, 2026.","identifier":"arXiv:2607.02841","url":"https://arxiv.org/abs/2607.02841"},{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine. ViNT: A Foundation Model for Visual Navigation. arXiv:2306.14846, 2023.","identifier":"arXiv:2306.14846","url":"https://arxiv.org/abs/2306.14846"},{"text":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"A. Eftekhar et al.. The One RING: a Robotic Indoor Navigation Generalist. arXiv:2412.14401, 2024.","identifier":"arXiv:2412.14401","url":"https://arxiv.org/abs/2412.14401"},{"text":"J. Zhang et al.. Embodied Navigation Foundation Model. arXiv:2509.12129, 2025.","identifier":"arXiv:2509.12129","url":"https://arxiv.org/abs/2509.12129"},{"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":"J. Zhang, Y. Du, X. Guo, S. Sun, X. Liu, Y. Sun, G. Lu, W. Sui, and J. Li. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness. arXiv:2607.19880, 2026.","identifier":"arXiv:2607.19880","url":"https://arxiv.org/abs/2607.19880"},{"text":"Open X-Embodiment Collaboration et al.. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv:2310.08864, 2023.","identifier":"arXiv:2310.08864","url":"https://arxiv.org/abs/2310.08864"},{"text":"Octo Model Team et al.. Octo: An Open-Source Generalist Robot Policy. arXiv:2405.12213, 2024.","identifier":"arXiv:2405.12213","url":"https://arxiv.org/abs/2405.12213"},{"text":"R. Doshi, H. Walke, O. Mees, S. Dasari, and S. Levine. Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation. arXiv:2408.11812, 2024.","identifier":"arXiv:2408.11812","url":"https://arxiv.org/abs/2408.11812"},{"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.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020.","identifier":"arXiv:2003.00368","url":"https://arxiv.org/abs/2003.00368"},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. arXiv:2303.01420, 2023.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"text":"Y. Kim, C. Kim, and J. Hwangbo. Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation. arXiv:2204.08647, 2022.","identifier":"arXiv:2204.08647","url":"https://arxiv.org/abs/2204.08647"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. arXiv:2209.12827, 2022.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. arXiv:2306.14874, 2023.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"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. arXiv:1901.08652, 2019.","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. arXiv:2010.11251, 2020.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. arXiv:2201.08117, 2022.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"G. Ji, J. Mun, H. Kim, and J. Hwangbo. Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion. arXiv:2202.05481, 2022.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"text":"G. B. Margolis and P. Agrawal. Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. arXiv:2212.03238, 2022.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath. Real-World Humanoid Locomotion with Reinforcement Learning. arXiv:2303.03381, 2023.","identifier":"arXiv:2303.03381","url":"https://arxiv.org/abs/2303.03381"},{"text":"K. Hausman, J. Preiss, G. S. Sukhatme, and S. Weiss. Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs. arXiv:1604.07905, 2016.","identifier":"arXiv:1604.07905","url":"https://arxiv.org/abs/1604.07905"},{"text":"E. S. Page. Continuous Inspection Schemes. Biometrika, 41(1/2):100–115, 1954.","identifier":"doi:10.1093/biomet/41.1-2.100","url":"https://doi.org/10.1093/biomet/41.1-2.100"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"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":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. arXiv:2305.08510, 2023.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. arXiv:2110.05113, 2021.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"A. Kendall and Y. Gal. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? arXiv:1703.04977, 2017.","identifier":"arXiv:1703.04977","url":"https://arxiv.org/abs/1703.04977"},{"text":"A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. arXiv:1903.11199, 2019.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"text":"M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. arXiv:2201.07067, 2022.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al.. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"text":"A. Kadian et al.. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? arXiv:1912.06321, 2019.","identifier":"arXiv:1912.06321","url":"https://arxiv.org/abs/1912.06321"},{"text":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2610.00003","metadata":{"title":"A Cross-Embodiment Navigation Stack for Heterogeneous Robot Teams: Planner, Interface Calibration, and Future-Range Perception","authors":[{"name":"Zhaoyu Li","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Jingyao Sun","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Yichen Wang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Bowen Huang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Tianyu Zhou","affiliation":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}],"publication_date":"2026-10-05","abstract":"Running one navigation stack on a heterogeneous robot team requires more than a planner that accepts different footprints: each robot's velocity interface drifts with payload and battery state, and range perception must be queried along the motions that the robot can actually execute. This paper presents a cross-embodiment navigation stack with three coupled layers that share a single data contract keyed on a versioned configuration descriptor. A configuration-driven local planner built on AgniNav plans over abstract robot configurations; an active interface calibration layer built on GAUGE keeps each robot's command-to-motion model current through planner-conditioned probes triggered by residual drift; and a control-aligned future-range layer built on FutureRay supplies free-range predictions along the directions each embodiment can execute under its calibrated interface. The stack was deployed on a mixed team of a Go2-class quadruped, a B2-class quadruped, and a wheeled robot on a three-floor indoor-adjacent course with doorways, a 12 cm step, and a light-degraded stairwell, during a 14 h campaign and short ablation runs. The full stack reached 88.2 percent end-to-end task success with a 28 ms p95 replanning latency. Removing any single layer lowered success by 3.0 to 10.6 percentage points, although the two recorded ablation summaries disagree on which layer matters most. A simulated humanoid embodiment was onboarded in 2.1 h and succeeded in 94 percent of 10 runs. The paper also characterizes the failure envelope of the stack.","references":[{"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":"T. Zang, H. Huang, S. Cheng, and W. Zhang. GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces. arXiv:2607.02841, 2026.","identifier":"arXiv:2607.02841","url":"https://arxiv.org/abs/2607.02841"},{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine. ViNT: A Foundation Model for Visual Navigation. arXiv:2306.14846, 2023.","identifier":"arXiv:2306.14846","url":"https://arxiv.org/abs/2306.14846"},{"text":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"A. Eftekhar et al.. The One RING: a Robotic Indoor Navigation Generalist. arXiv:2412.14401, 2024.","identifier":"arXiv:2412.14401","url":"https://arxiv.org/abs/2412.14401"},{"text":"J. Zhang et al.. Embodied Navigation Foundation Model. arXiv:2509.12129, 2025.","identifier":"arXiv:2509.12129","url":"https://arxiv.org/abs/2509.12129"},{"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":"J. Zhang, Y. Du, X. Guo, S. Sun, X. Liu, Y. Sun, G. Lu, W. Sui, and J. Li. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness. arXiv:2607.19880, 2026.","identifier":"arXiv:2607.19880","url":"https://arxiv.org/abs/2607.19880"},{"text":"Open X-Embodiment Collaboration et al.. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv:2310.08864, 2023.","identifier":"arXiv:2310.08864","url":"https://arxiv.org/abs/2310.08864"},{"text":"Octo Model Team et al.. Octo: An Open-Source Generalist Robot Policy. arXiv:2405.12213, 2024.","identifier":"arXiv:2405.12213","url":"https://arxiv.org/abs/2405.12213"},{"text":"R. Doshi, H. Walke, O. Mees, S. Dasari, and S. Levine. Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation. arXiv:2408.11812, 2024.","identifier":"arXiv:2408.11812","url":"https://arxiv.org/abs/2408.11812"},{"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.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020.","identifier":"arXiv:2003.00368","url":"https://arxiv.org/abs/2003.00368"},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. arXiv:2303.01420, 2023.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"text":"Y. Kim, C. Kim, and J. Hwangbo. Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation. arXiv:2204.08647, 2022.","identifier":"arXiv:2204.08647","url":"https://arxiv.org/abs/2204.08647"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. arXiv:2209.12827, 2022.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. arXiv:2306.14874, 2023.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"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. arXiv:1901.08652, 2019.","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. arXiv:2010.11251, 2020.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. arXiv:2201.08117, 2022.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"G. Ji, J. Mun, H. Kim, and J. Hwangbo. Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion. arXiv:2202.05481, 2022.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"text":"G. B. Margolis and P. Agrawal. Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. arXiv:2212.03238, 2022.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath. Real-World Humanoid Locomotion with Reinforcement Learning. arXiv:2303.03381, 2023.","identifier":"arXiv:2303.03381","url":"https://arxiv.org/abs/2303.03381"},{"text":"K. Hausman, J. Preiss, G. S. Sukhatme, and S. Weiss. Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs. arXiv:1604.07905, 2016.","identifier":"arXiv:1604.07905","url":"https://arxiv.org/abs/1604.07905"},{"text":"E. S. Page. Continuous Inspection Schemes. Biometrika, 41(1/2):100–115, 1954.","identifier":"doi:10.1093/biomet/41.1-2.100","url":"https://doi.org/10.1093/biomet/41.1-2.100"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"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":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. arXiv:2305.08510, 2023.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. arXiv:2110.05113, 2021.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"A. Kendall and Y. Gal. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? arXiv:1703.04977, 2017.","identifier":"arXiv:1703.04977","url":"https://arxiv.org/abs/1703.04977"},{"text":"A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. arXiv:1903.11199, 2019.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"text":"M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. arXiv:2201.07067, 2022.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al.. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"text":"A. Kadian et al.. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? arXiv:1912.06321, 2019.","identifier":"arXiv:1912.06321","url":"https://arxiv.org/abs/1912.06321"},{"text":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"}],"affiliation":null,"source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"status":"published","kagg_id":"KAGG:2610.00003","slug":"2610.00003","registered_at":"2026-10-06T09:07:00Z","updated_at":"2026-10-06T11:09:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2610.00003","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2610.00003","tex_path":"articles/2610.00003/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-10-06T11:24:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2610.00003"}