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. 2 (August 2026) > KAGG:2608.00009
Vol. 1, No. 2 (August 2026) — Article KAGG:2608.00009

Uncertainty-Aware Local Planning for Quadrupeds in Perceptually Degraded Environments

Authors: Jingyao Sun, Huiwen Cui, Zhaoyu Li, Bowen Huang

Legged Locomotion Group, Tsinghua University, Beijing, China

[Received 2026-08-07; accepted 2026-08-18; published online 2026-08-21]

Abstract:

Airborne dust, fog, and low light corrupt the range measurements on which a legged robot's local planner depends, and they tend to do so precisely when reactive collision avoidance matters most. This paper presents an uncertainty-aware local planner for quadrupeds that couples the configuration-driven AgniNav planning backbone with a FutureRay-style predictor of future free range along control-aligned directions. For every candidate command, the predictor outputs a point estimate and an uncertainty envelope that is recalibrated by degradation-stratified conformal prediction. When the control-aligned envelope width exceeds a threshold, the planner widens its safety margin and caps the commanded speed; below the threshold it reproduces the nominal policy exactly, and every modulated quantity is Lipschitz in the excess uncertainty. The planner was evaluated on an ANYmal in Isaac Lab across 15 scenes with sweeps over dust opacity (0 to 80 percent), illuminance (50 to 800 lx), and range dropout (0 to 40 percent), and in a dusty warehouse bay and a dim parking structure. It reached a success rate of 87.9 percent, compared with 76.9 percent for the nominal planner and 82.5 percent for fixed 1.5x margin inflation, while raising mean speed from 1.82 to 1.94 m/s, extending collision-free distance from 3.4 to 3.8 km per collision, and retaining 99.2 percent clean-regime parity. Removing envelope calibration lowered success to 74.0 percent, below the nominal planner. Recorded failure cases, including a 9 s LiDAR dropout, illustrate both the mechanism and its limits.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 2 (August 2026)
Article ID: KAGG:2608.00009
DOI: 10.5803/kagg.2608.00009
Received: 2026-08-07
Accepted: 2026-08-18
Published online: 2026-08-21
License: CC BY 4.0

References (43)

  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. Science Robotics, 2019. arXiv:1901.08652. arXiv:1901.08652
  2. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251. 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. Science Robotics, 2022. arXiv:2201.08117. arXiv:2201.08117
  4. M. Tranzatto et al.. CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge. Field Robotics, 2022. arXiv:2201.07067. arXiv:2201.07067
  5. A. Agha et al.. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021. arXiv:2103.11470
  6. K. Ebadi et al.. Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge. arXiv:2208.01787, 2022. arXiv:2208.01787
  7. M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide. Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. arXiv:1902.08913. arXiv:1902.08913
  8. M. Hahner, C. Sakaridis, D. Dai, and L. Van Gool. Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather. IEEE/CVF International Conference on Computer Vision (ICCV), 2021. arXiv:2108.05249. arXiv:2108.05249
  9. R. Heinzler, F. Piewak, P. Schindler, and W. Stork. CNN-based Lidar Point Cloud De-Noising in Adverse Weather. IEEE Robotics and Automation Letters, 2020. arXiv:1912.03874. arXiv:1912.03874
  10. 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
  11. 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
  12. A. Kendall and Y. Gal. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Advances in Neural Information Processing Systems (NeurIPS), 2017. arXiv:1703.04977. arXiv:1703.04977
  13. B. Lakshminarayanan, A. Pritzel, and C. Blundell. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. Advances in Neural Information Processing Systems (NeurIPS), 2017. arXiv:1612.01474. arXiv:1612.01474
  14. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. On Calibration of Modern Neural Networks. International Conference on Machine Learning (ICML), 2017. arXiv:1706.04599. arXiv:1706.04599
  15. V. Kuleshov, N. Fenner, and S. Ermon. Accurate Uncertainties for Deep Learning Using Calibrated Regression. International Conference on Machine Learning (ICML), 2018. arXiv:1807.00263. arXiv:1807.00263
  16. 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
  17. 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
  18. 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
  19. 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
  20. C. Cao, H. Zhu, F. Yang, Y. Xia, H. Choset, J. Oh, and J. Zhang. Autonomous Exploration Development Environment and the Planning Algorithms. IEEE International Conference on Robotics and Automation (ICRA), 2022. arXiv:2110.14573. arXiv:2110.14573
  21. 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, 2021. arXiv:2008.08835. arXiv:2008.08835
  22. 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
  23. 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), 2021. arXiv:2109.11978. arXiv:2109.11978
  24. 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), 2022. arXiv:2209.12827. arXiv:2209.12827
  25. D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874. arXiv:2306.14874
  26. L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. Field Robotics, 2023. arXiv:2303.01420. arXiv:2303.01420
  27. R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter. Perceptive Locomotion Through Nonlinear Model-Predictive Control. IEEE Transactions on Robotics, 2023. arXiv:2208.08373. arXiv:2208.08373
  28. T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022. arXiv:2204.12876. arXiv:2204.12876
  29. 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), 2023. arXiv:2305.08510. arXiv:2305.08510
  30. M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. arXiv:2203.15118. arXiv:2203.15118
  31. S. Zhao, H. Zhang, P. Wang, L. Nogueira, and S. Scherer. Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. arXiv:2104.14938. arXiv:2104.14938
  32. D. D. Fan, K. Otsu, Y. Kubo, A. Dixit, J. Burdick, and A.-A. Agha-Mohammadi. STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation. Robotics: Science and Systems (RSS), 2021. arXiv:2103.02828. arXiv:2103.02828
  33. Y. Gal and Z. Ghahramani. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. International Conference on Machine Learning (ICML), 2016. arXiv:1506.02142. arXiv:1506.02142
  34. A. N. Angelopoulos and S. Bates. A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification. arXiv:2107.07511, 2021. arXiv:2107.07511
  35. Y. Romano, E. Patterson, and E. J. Candès. Conformalized Quantile Regression. Advances in Neural Information Processing Systems (NeurIPS), 2019. arXiv:1905.03222. arXiv:1905.03222
  36. L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas. Safe Planning in Dynamic Environments using Conformal Prediction. IEEE Robotics and Automation Letters, 2023. arXiv:2210.10254. arXiv:2210.10254
  37. G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine. Uncertainty-Aware Reinforcement Learning for Collision Avoidance. arXiv:1702.01182, 2017. arXiv:1702.01182
  38. B. Lütjens, M. Everett, and J. P. How. Safe Reinforcement Learning with Model Uncertainty Estimates. IEEE International Conference on Robotics and Automation (ICRA), 2019. arXiv:1810.08700. arXiv:1810.08700
  39. A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. European Control Conference (ECC), 2019. arXiv:1903.11199. arXiv:1903.11199
  40. K. P. Wabersich and M. N. Zeilinger. A Predictive Safety Filter for Learning-Based Control of Constrained Nonlinear Dynamical Systems. Automatica, 2021. arXiv:1812.05506. arXiv:1812.05506
  41. K.-C. Hsu, H. Hu, and J. F. Fisac. The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems. arXiv:2309.05837, 2023. arXiv:2309.05837
  42. V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. NeurIPS Datasets and Benchmarks Track, 2021. arXiv:2108.10470. arXiv:2108.10470
  43. M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics and Automation Letters, 2023. arXiv:2301.04195. arXiv:2301.04195
Cite this article Jingyao Sun, Huiwen Cui, Zhaoyu Li, Bowen Huang, "Uncertainty-Aware Local Planning for Quadrupeds in Perceptually Degraded Environments," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 2, KAGG:2608.00009. DOI: 10.5803/kagg.2608.00009.