{"kagg_id":"KAGG:2607.00006","slug":"2607.00006","title":"Quantifying the Sim-to-Real Gap of Quadruped Velocity Controllers on Deformable Terrain","authors":[{"name":"Nikos Stavridis","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"},{"name":"Amelie Laurent","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"},{"name":"Diego Navarro","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"}],"affiliation":null,"abstract":"Sim-to-real studies of legged navigation usually attribute transfer failures to perception mismatch, yet on deformable terrain the velocity interface between planner and locomotion controller also changes character: response lag grows, effective gains shift, and saturation appears earlier. This paper quantifies that interface gap for a quadruped crossing terrain boundaries. We track a calibrated interface model online, using planner-conditioned probing in the style of GAUGE at 2 Hz, and couple it to a configuration-driven motion predictor in the style of AgniNav that scores, at 10 Hz, which fraction of the perceived free range is realizable under the current interface state. A sim-to-real pre-alignment step in Isaac Lab with matched terrain assets initializes the tracker for each terrain class. On an ANYmal traversing a course of grass, loose gravel, a sand trough, packed dirt, and grass (30 m per segment, 15 crossings per direction), the approach reduced interface drift from 4.9 to 4.6 percent per km relative to a static grass-calibrated model, raised range realizability from 70.4 to 74.8 percent, raised navigation success from 80.8 to 86.3 percent, and lowered 95th-percentile tracking error from 17.9 to 15.9 cm. Ablations indicate that online drift tracking, the realizability score, and pre-alignment each contribute. Recorded failures, including a 3.2-fold lag increase in sand and a transient realizability drop to 41 percent at the gravel-to-sand boundary, delimit where the diagnostics remain insufficient.","publication_date":"2026-07-22","registered_at":"2026-07-23T16:45:00Z","updated_at":"2026-07-23T19:45:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00006/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Hoepflinger. ANYmal - A Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7758092","url":"https://doi.org/10.1109/IROS.2016.7758092"},{"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":"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":"L. Wellhausen, A. Dosovitskiy, R. Ranftl, K. Walas, C. Cadena, and M. Hutter. Where Should I Walk? Predicting Terrain Properties from Images via Self-Supervised Learning. IEEE Robotics and Automation Letters, 4(2):1509-1516, 2019.","identifier":"doi:10.1109/LRA.2019.2895390","url":"https://doi.org/10.1109/LRA.2019.2895390"},{"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":"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":"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. 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. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"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":"M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke. Sim-to-Real: Learning Agile Locomotion for Quadruped Robots. arXiv:1804.10332, 2018.","identifier":"arXiv:1804.10332","url":"https://arxiv.org/abs/1804.10332"},{"text":"X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv:1710.06537, 2017.","identifier":"arXiv:1710.06537","url":"https://arxiv.org/abs/1710.06537"},{"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. arXiv:1703.06907, 2017.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"},{"text":"W. Yu, J. Tan, C. K. Liu, and G. Turk. Preparing for the Unknown: Learning a Universal Policy with Online System Identification. arXiv:1702.02453, 2017.","identifier":"arXiv:1702.02453","url":"https://arxiv.org/abs/1702.02453"},{"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":"A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn. Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning. arXiv:1803.11347, 2018.","identifier":"arXiv:1803.11347","url":"https://arxiv.org/abs/1803.11347"},{"text":"W. Zhao, J. P. Queralta, and T. Westerlund. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey. arXiv:2009.13303, 2020.","identifier":"arXiv:2009.13303","url":"https://arxiv.org/abs/2009.13303"},{"text":"S. Choi, G. Ji, J. Park, H. Kim, J. Mun, J. H. Lee, and J. Hwangbo. Learning Quadrupedal Locomotion on Deformable Terrain. Science Robotics, 8(74):eade2256, 2023.","identifier":"doi:10.1126/scirobotics.ade2256","url":"https://doi.org/10.1126/scirobotics.ade2256"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022.","identifier":"arXiv:2205.02824","url":"https://arxiv.org/abs/2205.02824"},{"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":"M. V. Gasparino, A. N. Sivakumar, Y. Liu, A. E. B. Velasquez, V. A. H. Higuti, J. Rogers, H. Tran, and G. Chowdhary. WayFAST: Navigation with Predictive Traversability in the Field. arXiv:2203.12071, 2022.","identifier":"arXiv:2203.12071","url":"https://arxiv.org/abs/2203.12071"},{"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":"P. Fankhauser, M. Bloesch, and M. Hutter. Probabilistic Terrain Mapping for Mobile Robots with Uncertain Localization. IEEE Robotics and Automation Letters, 3(4):3019-3026, 2018.","identifier":"doi:10.1109/LRA.2018.2849506","url":"https://doi.org/10.1109/LRA.2018.2849506"},{"text":"T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. arXiv:2204.12876, 2022.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"M. Wermelinger, P. Fankhauser, R. Diethelm, P. Krüsi, R. Siegwart, and M. Hutter. Navigation Planning for Legged Robots in Challenging Terrain. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7759199","url":"https://doi.org/10.1109/IROS.2016.7759199"},{"text":"L. Ljung. System Identification: Theory for the User, 2nd ed. Prentice Hall, 1999.","identifier":"ISBN:0136566952","url":"https://openlibrary.org/isbn/0136566952"},{"text":"K. J. Åström and B. Wittenmark. Adaptive Control, 2nd ed. Addison-Wesley, 1995.","identifier":"ISBN:0201558661","url":"https://openlibrary.org/isbn/0201558661"},{"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":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. arXiv:2102.12667, 2021.","identifier":"arXiv:2102.12667","url":"https://arxiv.org/abs/2102.12667"},{"text":"H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas. VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics. arXiv:2203.15983, 2022.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"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":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"doi:10.1109/ICRA.2017.7989202","url":"https://doi.org/10.1109/ICRA.2017.7989202"},{"text":"D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. arXiv:2103.04351, 2021.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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. Truong, M. Rudolph, N. H. Yokoyama, S. Chernova, D. Batra, and A. Rai. Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation. Proceedings of Machine Learning Research, 205:859-870, 2023.","identifier":"PMLR:v205/truong23a","url":"https://proceedings.mlr.press/v205/truong23a.html"},{"text":"M. Bloesch, M. Hutter, M. A. Hoepflinger, S. Leutenegger, C. Gehring, C. D. Remy, and R. Siegwart. State Estimation for Legged Robots: Consistent Fusion of Leg Kinematics and IMU. Robotics: Science and Systems VIII, 2012.","identifier":"doi:10.15607/RSS.2012.VIII.003","url":"https://doi.org/10.15607/RSS.2012.VIII.003"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00006","metadata":{"title":"Quantifying the Sim-to-Real Gap of Quadruped Velocity Controllers on Deformable Terrain","authors":[{"name":"Nikos Stavridis","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"},{"name":"Amelie Laurent","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"},{"name":"Diego Navarro","affiliation":"Robot Dynamics Group, ETH Zurich, Zurich, Switzerland"}],"publication_date":"2026-07-22","abstract":"Sim-to-real studies of legged navigation usually attribute transfer failures to perception mismatch, yet on deformable terrain the velocity interface between planner and locomotion controller also changes character: response lag grows, effective gains shift, and saturation appears earlier. This paper quantifies that interface gap for a quadruped crossing terrain boundaries. We track a calibrated interface model online, using planner-conditioned probing in the style of GAUGE at 2 Hz, and couple it to a configuration-driven motion predictor in the style of AgniNav that scores, at 10 Hz, which fraction of the perceived free range is realizable under the current interface state. A sim-to-real pre-alignment step in Isaac Lab with matched terrain assets initializes the tracker for each terrain class. On an ANYmal traversing a course of grass, loose gravel, a sand trough, packed dirt, and grass (30 m per segment, 15 crossings per direction), the approach reduced interface drift from 4.9 to 4.6 percent per km relative to a static grass-calibrated model, raised range realizability from 70.4 to 74.8 percent, raised navigation success from 80.8 to 86.3 percent, and lowered 95th-percentile tracking error from 17.9 to 15.9 cm. Ablations indicate that online drift tracking, the realizability score, and pre-alignment each contribute. Recorded failures, including a 3.2-fold lag increase in sand and a transient realizability drop to 41 percent at the gravel-to-sand boundary, delimit where the diagnostics remain insufficient.","references":[{"text":"M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Hoepflinger. ANYmal - A Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7758092","url":"https://doi.org/10.1109/IROS.2016.7758092"},{"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":"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":"L. Wellhausen, A. Dosovitskiy, R. Ranftl, K. Walas, C. Cadena, and M. Hutter. Where Should I Walk? Predicting Terrain Properties from Images via Self-Supervised Learning. IEEE Robotics and Automation Letters, 4(2):1509-1516, 2019.","identifier":"doi:10.1109/LRA.2019.2895390","url":"https://doi.org/10.1109/LRA.2019.2895390"},{"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":"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":"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. 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. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"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":"M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"J. Tan, T. Zhang, E. Coumans, A. Iscen, Y. Bai, D. Hafner, S. Bohez, and V. Vanhoucke. Sim-to-Real: Learning Agile Locomotion for Quadruped Robots. arXiv:1804.10332, 2018.","identifier":"arXiv:1804.10332","url":"https://arxiv.org/abs/1804.10332"},{"text":"X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv:1710.06537, 2017.","identifier":"arXiv:1710.06537","url":"https://arxiv.org/abs/1710.06537"},{"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. arXiv:1703.06907, 2017.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"},{"text":"W. Yu, J. Tan, C. K. Liu, and G. Turk. Preparing for the Unknown: Learning a Universal Policy with Online System Identification. arXiv:1702.02453, 2017.","identifier":"arXiv:1702.02453","url":"https://arxiv.org/abs/1702.02453"},{"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":"A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn. Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning. arXiv:1803.11347, 2018.","identifier":"arXiv:1803.11347","url":"https://arxiv.org/abs/1803.11347"},{"text":"W. Zhao, J. P. Queralta, and T. Westerlund. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey. arXiv:2009.13303, 2020.","identifier":"arXiv:2009.13303","url":"https://arxiv.org/abs/2009.13303"},{"text":"S. Choi, G. Ji, J. Park, H. Kim, J. Mun, J. H. Lee, and J. Hwangbo. Learning Quadrupedal Locomotion on Deformable Terrain. Science Robotics, 8(74):eade2256, 2023.","identifier":"doi:10.1126/scirobotics.ade2256","url":"https://doi.org/10.1126/scirobotics.ade2256"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022.","identifier":"arXiv:2205.02824","url":"https://arxiv.org/abs/2205.02824"},{"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":"M. V. Gasparino, A. N. Sivakumar, Y. Liu, A. E. B. Velasquez, V. A. H. Higuti, J. Rogers, H. Tran, and G. Chowdhary. WayFAST: Navigation with Predictive Traversability in the Field. arXiv:2203.12071, 2022.","identifier":"arXiv:2203.12071","url":"https://arxiv.org/abs/2203.12071"},{"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":"P. Fankhauser, M. Bloesch, and M. Hutter. Probabilistic Terrain Mapping for Mobile Robots with Uncertain Localization. IEEE Robotics and Automation Letters, 3(4):3019-3026, 2018.","identifier":"doi:10.1109/LRA.2018.2849506","url":"https://doi.org/10.1109/LRA.2018.2849506"},{"text":"T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. arXiv:2204.12876, 2022.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"M. Wermelinger, P. Fankhauser, R. Diethelm, P. Krüsi, R. Siegwart, and M. Hutter. Navigation Planning for Legged Robots in Challenging Terrain. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7759199","url":"https://doi.org/10.1109/IROS.2016.7759199"},{"text":"L. Ljung. System Identification: Theory for the User, 2nd ed. Prentice Hall, 1999.","identifier":"ISBN:0136566952","url":"https://openlibrary.org/isbn/0136566952"},{"text":"K. J. Åström and B. Wittenmark. Adaptive Control, 2nd ed. Addison-Wesley, 1995.","identifier":"ISBN:0201558661","url":"https://openlibrary.org/isbn/0201558661"},{"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":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. arXiv:2102.12667, 2021.","identifier":"arXiv:2102.12667","url":"https://arxiv.org/abs/2102.12667"},{"text":"H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas. VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics. arXiv:2203.15983, 2022.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"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":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"doi:10.1109/ICRA.2017.7989202","url":"https://doi.org/10.1109/ICRA.2017.7989202"},{"text":"D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. arXiv:2103.04351, 2021.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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. Truong, M. Rudolph, N. H. Yokoyama, S. Chernova, D. Batra, and A. Rai. Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation. Proceedings of Machine Learning Research, 205:859-870, 2023.","identifier":"PMLR:v205/truong23a","url":"https://proceedings.mlr.press/v205/truong23a.html"},{"text":"M. Bloesch, M. 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