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:2607.00009
Vol. 1, No. 1 (July 2026) — Article KAGG:2607.00009

Predictive Contact-Aware Path Following for Agile Quadrupeds in Narrow Passages

Authors: Felix Wagner, Ananya Iyer, Meilin Jiang, Carlos Ramirez

Legged Robotics Lab, Stanford University, Stanford, CA, USA

[Received 2026-07-16; accepted 2026-07-27; published online 2026-07-30]

Abstract:

In narrow passages, the contact sequence of a quadruped's legs and the lag between commanded and realized body velocity decide whether the robot clears the gap, yet most path followers treat the body as a point with ideal velocity response. This paper presents a predictive contact-aware path follower that couples a calibrated model of the opaque velocity interface, obtained with GAUGE, with the configuration-based motion prediction of AgniNav. For each candidate command sequence, the follower predicts the realized body trajectory, the gait phase, and the swept leg envelope over a 0.5-1.0 s horizon; it schedules body poses and contact phases so that the legs nearest the passage boundary enter in a favorable phase, and it rejects passage entries whose predicted trajectory cannot clear the gap within the clearance and time budgets. The approach is evaluated in an Isaac Lab narrow-gate suite with doorway-scale gaps of 55-90 cm, rope barriers, and pipe runs, and in Unitree Go2 trials through a 72 cm doorway-scale gap and two rope barriers at setpoints of 1.0-3.0 m/s. Compared with point-body path following under an ideal velocity assumption, the method raises first-attempt success from 75.5 to 84.6 percent and shortens time to pass from 3.2 to 2.8 s, at the cost of a smaller mean clearance margin of 13.2 cm versus 15.1 cm. Removing contact-phase scheduling, future-range rejection, or the calibrated interface model reduces success to between 72.4 and 74.4 percent.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 1 (July 2026)
Article ID: KAGG:2607.00009
DOI: 10.5803/kagg.2607.00009
Received: 2026-07-16
Accepted: 2026-07-27
Published online: 2026-07-30
License: CC BY 4.0

References (36)

  1. S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020. arXiv:2003.00368
  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. 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
  5. 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
  6. 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
  7. 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
  8. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021. arXiv:2107.04034
  9. 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
  10. G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022. arXiv:2205.02824
  11. 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
  12. F. Farshidian, E. Jelavic, A. Satapathy, M. Giftthaler, and J. Buchli. Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach. arXiv:1710.04029, 2017. arXiv:1710.04029
  13. D. Kim, J. Di Carlo, B. Katz, G. Bledt, and S. Kim. Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control. arXiv:1909.06586, 2019. arXiv:1909.06586
  14. R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter. Perceptive Locomotion through Nonlinear Model Predictive Control. arXiv:2208.08373, 2022. arXiv:2208.08373
  15. F. Jenelten, J. He, F. Farshidian, and M. Hutter. DTC: Deep Tracking Control. arXiv:2309.15462, 2023. arXiv:2309.15462
  16. 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
  17. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. arXiv:2306.14874, 2023. arXiv:2306.14874
  18. Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. arXiv:2309.05665, 2023. arXiv:2309.05665
  19. X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023. arXiv:2309.14341
  20. A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022. arXiv:2211.07638
  21. Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. arXiv:2112.02094, 2021. arXiv:2112.02094
  22. J. Truong, D. Yarats, T. Li, F. Meier, S. Chernova, D. Batra, and A. Rai. Learning Navigation Skills for Legged Robots with Learned Robot Embeddings. arXiv:2011.12255, 2020. arXiv:2011.12255
  23. J. Frey, D. Hoeller, S. Khattak, and M. Hutter. Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments. arXiv:2203.15854, 2022. arXiv:2203.15854
  24. 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
  25. G. Williams, A. Aldrich, and E. Theodorou. Model Predictive Path Integral Control using Covariance Variable Importance Sampling. arXiv:1509.01149, 2015. arXiv:1509.01149
  26. G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020. arXiv:2002.05700
  27. D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022. arXiv:2210.03370
  28. 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. arXiv:2306.14846
  29. A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023. arXiv:2310.07896
  30. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. arXiv:1903.11199, 2019. arXiv:1903.11199
  31. 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
  32. 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
  33. V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State. Isaac Gym: High Performance GPU-Based Physics Simulation for Robot Learning. arXiv:2108.10470, 2021. arXiv:2108.10470
  34. M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. arXiv:2301.04195, 2023. arXiv:2301.04195
  35. A. Kadian, J. Truong, A. Gokaslan, A. Clegg, E. Wijmans, S. Lee, M. Savva, S. Chernova, and D. Batra. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?. arXiv:1912.06321, 2019. arXiv:1912.06321
  36. N. Yokoyama, S. Ha, and D. Batra. Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation. arXiv:2103.08022, 2021. arXiv:2103.08022
Cite this article Felix Wagner, Ananya Iyer, Meilin Jiang, Carlos Ramirez, "Predictive Contact-Aware Path Following for Agile Quadrupeds in Narrow Passages," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 1, KAGG:2607.00009. DOI: 10.5803/kagg.2607.00009.