Vol. 1, No. 2 (August 2026) — Article KAGG:2608.00008

Probing Opaque Velocity Interfaces on Production Quadruped Platforms: A Field Study

Authors: Andrew Foster, Lena Vasquez, Rajiv Menon

Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA

[Received 2026-08-04; accepted 2026-08-15; published online 2026-08-18]

Abstract:

Production quadruped platforms expose velocity command interfaces whose internals (gait managers, safety layers, terrain modes) are undocumented, yet navigation planners routinely assume that commanded velocities are tracked instantly and uniformly. This paper reports a 14-day field characterization of three commercial quadruped platforms, two 70-class research units and one 45-class work unit, across laboratory, campus-path, and light outdoor terrain using GAUGE-style planner-conditioned active calibration. Each platform received 60 probe sessions (20 per terrain mode) logged at 100 Hz, with 0, 5, and 10 kg payloads rotated across sessions. We report command fidelity, 95th-percentile phase lag, saturation-envelope error, and mode-switch transients. On the primary unit with the full probe suite, the command fidelity RMS error is 9.7 percent with a p95 phase lag of 71 ms, compared with 11.1 percent and 79 ms on Platform B and 10.6 percent and 81 ms on Platform C. Restricting probes to the default mode raises the error to 10.5 percent, and removing disturbance rejection raises it to 10.7 percent. Payload is the dominant degradation factor: from 0 to 10 kg, the fidelity error rises from 8.9 to 12.6 percent and the phase lag from 64 to 88 ms. Probes also exposed three behaviors that break naive planning assumptions: an undocumented 3.2 m/s speed limiter in an outdoor mode, a 0.7 s gait-manager hysteresis that produced 0.4 m overshoot, and an unannounced careful mode under a 10 kg payload that halves the usable envelope. We derive interface assumptions that planners for such platforms should state explicitly.


References (40)

  1. 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
  2. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. arXiv:2010.11251, 2020. arXiv:2010.11251
  3. 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
  4. G. Williams, A. Aldrich, and E. Theodorou. Model Predictive Path Integral Control using Covariance Variable Importance Sampling. arXiv:1509.01149, 2015. arXiv:1509.01149
  5. 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
  6. 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
  7. 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
  8. 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
  9. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021. arXiv:2107.04034
  10. 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
  11. K. Chua, R. Calandra, R. McAllister, and S. Levine. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. arXiv:1805.12114, 2018. arXiv:1805.12114
  12. T. Lew, A. Sharma, J. Harrison, A. Bylard, and M. Pavone. Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework. arXiv:2008.11700, 2020. arXiv:2008.11700
  13. T. Koller, F. Berkenkamp, M. Turchetta, and A. Krause. Learning-Based Model Predictive Control for Safe Exploration. arXiv:1803.08287, 2018. arXiv:1803.08287
  14. 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
  15. 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
  16. 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
  17. Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg. Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. arXiv:2011.02404, 2020. arXiv:2011.02404
  18. Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox. Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience. arXiv:1810.05687, 2018. arXiv:1810.05687
  19. F. Ramos, R. C. Possas, and D. Fox. BayesSim: Adaptive Domain Randomization via Probabilistic Inference for Robotics Simulators. arXiv:1906.01728, 2019. arXiv:1906.01728
  20. 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
  21. G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022. arXiv:2205.02824
  22. 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
  23. Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots. Fast and Efficient Locomotion via Learned Gait Transitions. arXiv:2104.04644, 2021. arXiv:2104.04644
  24. A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022. arXiv:2211.07638
  25. X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023. arXiv:2309.14341
  26. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. arXiv:2306.14874, 2023. arXiv:2306.14874
  27. X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. arXiv:2011.13112, 2020. arXiv:2011.13112
  28. 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
  29. 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
  30. G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020. arXiv:2002.05700
  31. 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
  32. 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
  33. 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
  34. 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
  35. J. Lee, M. Bjelonic, A. Reske, L. Wellhausen, T. Miki, and M. Hutter. Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots. arXiv:2405.01792, 2024. arXiv:2405.01792
  36. 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
  37. 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
  38. 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
  39. S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. arXiv:1709.07523, 2017. arXiv:1709.07523
  40. L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning. arXiv:2108.06266, 2021. arXiv:2108.06266
Cite this article Andrew Foster, Lena Vasquez, Rajiv Menon, "Probing Opaque Velocity Interfaces on Production Quadruped Platforms: A Field Study," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 2, KAGG:2608.00008. DOI: 10.5803/kagg.2608.00008.