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TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 4 (October 2026) > KAGG:2610.00002
Vol. 1, No. 4 (October 2026) — Article KAGG:2610.00002

Range-Aligned Control Allocation for Agile Quadruped Navigation

Authors: Jonas Meier, Diego Navarro, Clara Fischer

Robot Dynamics Group, ETH Zurich, Zurich, Switzerland

[Received 2026-09-19; accepted 2026-09-30; published online 2026-10-03]

Abstract:

Whole-body controllers for quadrupedal robots typically distribute contact forces without knowledge of where the next obstacle will be, so stability reserves and swing clearance are allocated uniformly rather than where the upcoming motion needs them. This paper presents range-aligned control allocation, which uses a predicted future range field, computed in the style of FutureRay along the control-aligned directions of the robot, to set the weights of a whole-body quadratic program. A softmin over the field locates the predicted closest approach; stance-force weights and a linear load-shift term then bias stabilizing support toward the side opposite that approach, and a barrier-type constraint enforces swing-foot clearance only inside the predicted path corridor. All range-dependent terms scale with a field-contrast statistic that vanishes exactly when the field is uniform, and we prove that the allocator then reduces to the nominal controller, with solution deviation otherwise bounded linearly in the contrast. In an evaluation on ANYmal comprising Isaac Lab simulation across ten scenes with raised obstacles of 15 to 60 cm at 1.0 to 3.0 m/s and hardware runs beside raised obstacles, the method lowered energy per kilometer from 1334 to 1238 J/km relative to range-agnostic allocation, reduced the 95th-percentile solve time from 2.9 to 2.6 ms, and matched the unweighted controller in 99.6 percent of uniform-field evaluations. Its mean clearance margin, however, was lower (20.8 versus 24.0 cm), so the results indicate a tighter, less conservative allocation rather than uniformly larger clearance.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 4 (October 2026)
Article ID: KAGG:2610.00002
DOI: 10.5803/kagg.2610.00002
Received: 2026-09-19
Accepted: 2026-09-30
Published online: 2026-10-03
License: CC BY 4.0

References (44)

  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. C. D. Bellicoso, F. Jenelten, C. Gehring, and M. Hutter. Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots. IEEE Robotics and Automation Letters, 3(3):2261-2268, 2018. doi:10.1109/LRA.2018.2794620
  3. 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
  4. R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter. Perceptive Locomotion through Nonlinear Model Predictive Control. IEEE Transactions on Robotics, arXiv:2208.08373, 2023. arXiv:2208.08373
  5. J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, arXiv:1901.08652, 2019. arXiv:1901.08652
  6. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, arXiv:2010.11251, 2020. arXiv:2010.11251
  7. T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, arXiv:2201.08117, 2022. arXiv:2201.08117
  8. F. Jenelten, J. He, F. Farshidian, and M. Hutter. DTC: Deep Tracking Control. Science Robotics, arXiv:2309.15462, 2024. arXiv:2309.15462
  9. C. D. Bellicoso, C. Gehring, J. Hwangbo, P. Fankhauser, and M. Hutter. Perception-less Terrain Adaptation through Whole Body Control and Hierarchical Optimization. IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2016. doi:10.1109/HUMANOIDS.2016.7803330
  10. A. Herzog, N. Rotella, S. Mason, F. Grimminger, S. Schaal, and L. Righetti. Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid. Autonomous Robots, arXiv:1410.7284, 2016. arXiv:1410.7284
  11. A. Escande, N. Mansard, and P.-B. Wieber. Hierarchical Quadratic Programming: Fast Online Humanoid-Robot Motion Generation. The International Journal of Robotics Research, 33(7):1006-1028, 2014. doi:10.1177/0278364914521306
  12. T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026. arXiv:2608.01197
  13. S. Kuindersma, R. Deits, M. Fallon, A. Valenzuela, H. Dai, F. Permenter, T. Koolen, P. Marion, and R. Tedrake. Optimization-based Locomotion Planning, Estimation, and Control Design for the Atlas Humanoid Robot. Autonomous Robots, 40(3):429-455, 2016. doi:10.1007/s10514-015-9479-3
  14. C. Gehring, S. Coros, M. Hutter, C. D. Bellicoso, H. Heijnen, R. Diethelm, M. Bloesch, P. Fankhauser, J. Hwangbo, M. Hoepflinger, and R. Siegwart. Practice Makes Perfect: An Optimization-Based Approach to Controlling Agile Motions for a Quadruped Robot. IEEE Robotics and Automation Magazine, 23(1):34-43, 2016. doi:10.1109/MRA.2015.2505910
  15. J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. doi:10.1109/IROS.2018.8594448
  16. S. Caron, Q.-C. Pham, and Y. Nakamura. Stability of Surface Contacts for Humanoid Robots: Closed-Form Formulae of the Contact Wrench Cone for Rectangular Support Areas. IEEE International Conference on Robotics and Automation (ICRA), arXiv:1501.04719, 2015. arXiv:1501.04719
  17. B. Stellato, G. Banjac, P. Goulart, A. Bemporad, and S. Boyd. OSQP: An Operator Splitting Solver for Quadratic Programs. Mathematical Programming Computation, arXiv:1711.08013, 2020. arXiv:1711.08013
  18. F. Jenelten, R. Grandia, F. Farshidian, and M. Hutter. TAMOLS: Terrain-Aware Motion Optimization for Legged Systems. IEEE Transactions on Robotics, arXiv:2206.14049, 2022. arXiv:2206.14049
  19. C. Mastalli, R. Budhiraja, W. Merkt, G. Saurel, B. Hammoud, M. Naveau, J. Carpentier, L. Righetti, S. Vijayakumar, and N. Mansard. Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control. IEEE International Conference on Robotics and Automation (ICRA), arXiv:1909.04947, 2020. arXiv:1909.04947
  20. N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. Conference on Robot Learning (CoRL), 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. Robotics: Science and Systems (RSS), arXiv:2205.02824, 2022. arXiv:2205.02824
  22. A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems (RSS), arXiv:2107.04034, 2021. arXiv:2107.04034
  23. A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. Conference on Robot Learning (CoRL), arXiv:2211.07638, 2022. arXiv:2211.07638
  24. Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. Conference on Robot Learning (CoRL), arXiv:2309.05665, 2023. arXiv:2309.05665
  25. X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. IEEE International Conference on Robotics and Automation (ICRA), arXiv:2309.14341, 2024. arXiv:2309.14341
  26. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, arXiv:2306.14874, 2024. arXiv:2306.14874
  27. 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
  28. J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems (RSS), arXiv:2305.08510, 2023. arXiv:2305.08510
  29. L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. Field Robotics, arXiv:2303.01420, 2023. arXiv:2303.01420
  30. M. Tranzatto et al.. CERBERUS in the DARPA Subterranean Challenge. Science Robotics, arXiv:2201.07067, 2022. arXiv:2201.07067
  31. D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, arXiv:2103.04351, 2021. arXiv:2103.04351
  32. N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), arXiv:2209.12827, 2022. arXiv:2209.12827
  33. 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
  34. 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. arXiv:2603.19602
  35. J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026. arXiv:2607.18200
  36. 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
  37. N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa. CHOMP: Gradient Optimization Techniques for Efficient Motion Planning. IEEE International Conference on Robotics and Automation (ICRA), 2009. doi:10.1109/ROBOT.2009.5152817
  38. X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao. EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors. IEEE Robotics and Automation Letters, arXiv:2008.08835, 2021. arXiv:2008.08835
  39. A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada. Control Barrier Function Based Quadratic Programs for Safety Critical Systems. IEEE Transactions on Automatic Control, arXiv:1609.06408, 2017. arXiv:1609.06408
  40. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. European Control Conference (ECC), arXiv:1903.11199, 2019. arXiv:1903.11199
  41. W. Xiao and C. Belta. Control Barrier Functions for Systems with High Relative Degree. IEEE Conference on Decision and Control (CDC), arXiv:1903.04706, 2019. arXiv:1903.04706
  42. R. Grandia, A. J. Taylor, A. D. Ames, and M. Hutter. Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control. IEEE International Conference on Robotics and Automation (ICRA), arXiv:2011.00032, 2021. arXiv:2011.00032
  43. 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
  44. 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. IEEE Robotics and Automation Letters, arXiv:2301.04195, 2023. arXiv:2301.04195
Cite this article Jonas Meier, Diego Navarro, Clara Fischer, "Range-Aligned Control Allocation for Agile Quadruped Navigation," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 4, KAGG:2610.00002. DOI: 10.5803/kagg.2610.00002.