ISSN 3083-4471 API Registry JSON Sitemap
TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 1 (July 2026) > KAGG:2607.00006
Vol. 1, No. 1 (July 2026) — Article KAGG:2607.00006

Quantifying the Sim-to-Real Gap of Quadruped Velocity Controllers on Deformable Terrain

Authors: Nikos Stavridis, Amelie Laurent, Diego Navarro

Robot Dynamics Group, ETH Zurich, Zurich, Switzerland

[Received 2026-07-08; accepted 2026-07-19; published online 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.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 1 (July 2026)
Article ID: KAGG:2607.00006
DOI: 10.5803/kagg.2607.00006
Received: 2026-07-08
Accepted: 2026-07-19
Published online: 2026-07-22
License: CC BY 4.0

References (40)

  1. 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. doi:10.1109/IROS.2016.7758092
  2. 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. arXiv:1901.08652
  3. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. arXiv:2010.11251, 2020. arXiv:2010.11251
  4. 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. arXiv:2201.08117
  5. 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. doi:10.1109/LRA.2019.2895390
  6. J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. arXiv:2305.08510, 2023. arXiv:2305.08510
  7. A. Kadian et al.. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? arXiv:1912.06321, 2019. arXiv:1912.06321
  8. T. Zang, H. Huang, S. Cheng, and W. Zhang. GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces. arXiv:2607.02841, 2026. arXiv:2607.02841
  9. 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. arXiv:2606.10903
  10. N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021. arXiv:2109.11978
  11. V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation for Robot Learning. arXiv:2108.10470, 2021. arXiv:2108.10470
  12. 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. arXiv:2301.04195
  13. 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. arXiv:1804.10332
  14. X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv:1710.06537, 2017. arXiv:1710.06537
  15. 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. arXiv:1703.06907
  16. 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. arXiv:1702.02453
  17. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021. arXiv:2107.04034
  18. 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. arXiv:2202.05481
  19. 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. arXiv:1803.11347
  20. W. Zhao, J. P. Queralta, and T. Westerlund. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey. arXiv:2009.13303, 2020. arXiv:2009.13303
  21. 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. doi:10.1126/scirobotics.ade2256
  22. G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022. arXiv:2205.02824
  23. G. B. Margolis and P. Agrawal. Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. arXiv:2212.03238, 2022. arXiv:2212.03238
  24. 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. arXiv:2203.12071
  25. G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020. arXiv:2002.05700
  26. 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. doi:10.1109/LRA.2018.2849506
  27. 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. arXiv:2204.12876
  28. 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. doi:10.1109/IROS.2016.7759199
  29. L. Ljung. System Identification: Theory for the User, 2nd ed. Prentice Hall, 1999. ISBN:0136566952
  30. K. J. Åström and B. Wittenmark. Adaptive Control, 2nd ed. Addison-Wesley, 1995. ISBN:0201558661
  31. E. S. Page. Continuous Inspection Schemes. Biometrika, 41(1/2):100-115, 1954. doi:10.1093/biomet/41.1-2.100
  32. X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. arXiv:2102.12667, 2021. arXiv:2102.12667
  33. 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. arXiv:2203.15983
  34. 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
  35. 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. doi:10.1109/ICRA.2017.7989202
  36. D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. arXiv:2103.04351, 2021. arXiv:2103.04351
  37. N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. arXiv:2209.12827, 2022. arXiv:2209.12827
  38. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. arXiv:2306.14874, 2023. arXiv:2306.14874
  39. 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. PMLR:v205/truong23a
  40. 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. doi:10.15607/RSS.2012.VIII.003
Cite this article Nikos Stavridis, Amelie Laurent, Diego Navarro, "Quantifying the Sim-to-Real Gap of Quadruped Velocity Controllers on Deformable Terrain," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 1, KAGG:2607.00006. DOI: 10.5803/kagg.2607.00006.