{"kagg_id":"KAGG:2608.00009","slug":"2608.00009","title":"Uncertainty-Aware Local Planning for Quadrupeds in Perceptually Degraded Environments","authors":[{"name":"Jingyao Sun","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Huiwen Cui","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Zhaoyu Li","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Bowen Huang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"}],"affiliation":null,"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.","publication_date":"2026-08-21","registered_at":"2026-08-24T17:23:00Z","updated_at":"2026-08-25T13:09:00Z","status":"published","html_path":null,"pdf_path":"articles/2608.00009/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"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.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"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.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"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.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al.. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"text":"K. Ebadi et al.. Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge. arXiv:2208.01787, 2022.","identifier":"arXiv:2208.01787","url":"https://arxiv.org/abs/2208.01787"},{"text":"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.","identifier":"arXiv:1902.08913","url":"https://arxiv.org/abs/1902.08913"},{"text":"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.","identifier":"arXiv:2108.05249","url":"https://arxiv.org/abs/2108.05249"},{"text":"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.","identifier":"arXiv:1912.03874","url":"https://arxiv.org/abs/1912.03874"},{"text":"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.","identifier":"arXiv:2606.10903","url":"https://arxiv.org/abs/2606.10903"},{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"text":"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.","identifier":"arXiv:1703.04977","url":"https://arxiv.org/abs/1703.04977"},{"text":"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.","identifier":"arXiv:1612.01474","url":"https://arxiv.org/abs/1612.01474"},{"text":"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.","identifier":"arXiv:1706.04599","url":"https://arxiv.org/abs/1706.04599"},{"text":"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.","identifier":"arXiv:1807.00263","url":"https://arxiv.org/abs/1807.00263"},{"text":"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.","identifier":"arXiv:2603.19602","url":"https://arxiv.org/abs/2603.19602"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"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.","identifier":"arXiv:2306.14846","url":"https://arxiv.org/abs/2306.14846"},{"text":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"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.","identifier":"arXiv:2110.14573","url":"https://arxiv.org/abs/2110.14573"},{"text":"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.","identifier":"arXiv:2008.08835","url":"https://arxiv.org/abs/2008.08835"},{"text":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"text":"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.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"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.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. Field Robotics, 2023. arXiv:2303.01420.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"text":"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.","identifier":"arXiv:2208.08373","url":"https://arxiv.org/abs/2208.08373"},{"text":"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.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"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.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"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.","identifier":"arXiv:2203.15118","url":"https://arxiv.org/abs/2203.15118"},{"text":"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.","identifier":"arXiv:2104.14938","url":"https://arxiv.org/abs/2104.14938"},{"text":"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.","identifier":"arXiv:2103.02828","url":"https://arxiv.org/abs/2103.02828"},{"text":"Y. Gal and Z. Ghahramani. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. 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Hu, and J. F. Fisac. The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems. arXiv:2309.05837, 2023.","identifier":"arXiv:2309.05837","url":"https://arxiv.org/abs/2309.05837"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. NeurIPS Datasets and Benchmarks Track, 2021. arXiv:2108.10470.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. IEEE Robotics and Automation Letters, 2023. arXiv:2301.04195.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00009","metadata":{"title":"Uncertainty-Aware Local Planning for Quadrupeds in Perceptually Degraded Environments","authors":[{"name":"Jingyao Sun","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Huiwen Cui","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Zhaoyu Li","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"},{"name":"Bowen Huang","affiliation":"Legged Locomotion Group, Tsinghua University, Beijing, China"}],"publication_date":"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.","references":[{"text":"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.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"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.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"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.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al.. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"text":"K. Ebadi et al.. Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge. arXiv:2208.01787, 2022.","identifier":"arXiv:2208.01787","url":"https://arxiv.org/abs/2208.01787"},{"text":"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.","identifier":"arXiv:1902.08913","url":"https://arxiv.org/abs/1902.08913"},{"text":"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.","identifier":"arXiv:2108.05249","url":"https://arxiv.org/abs/2108.05249"},{"text":"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.","identifier":"arXiv:1912.03874","url":"https://arxiv.org/abs/1912.03874"},{"text":"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.","identifier":"arXiv:2606.10903","url":"https://arxiv.org/abs/2606.10903"},{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"text":"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.","identifier":"arXiv:1703.04977","url":"https://arxiv.org/abs/1703.04977"},{"text":"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.","identifier":"arXiv:1612.01474","url":"https://arxiv.org/abs/1612.01474"},{"text":"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.","identifier":"arXiv:1706.04599","url":"https://arxiv.org/abs/1706.04599"},{"text":"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.","identifier":"arXiv:1807.00263","url":"https://arxiv.org/abs/1807.00263"},{"text":"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.","identifier":"arXiv:2603.19602","url":"https://arxiv.org/abs/2603.19602"},{"text":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. arXiv:2210.03370, 2022.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"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.","identifier":"arXiv:2306.14846","url":"https://arxiv.org/abs/2306.14846"},{"text":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"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.","identifier":"arXiv:2110.14573","url":"https://arxiv.org/abs/2110.14573"},{"text":"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.","identifier":"arXiv:2008.08835","url":"https://arxiv.org/abs/2008.08835"},{"text":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"text":"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.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"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.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. Hoeller, N. Rudin, D. Sako, and M. Hutter. ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots. Science Robotics, 2024. arXiv:2306.14874.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. Field Robotics, 2023. arXiv:2303.01420.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"text":"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.","identifier":"arXiv:2208.08373","url":"https://arxiv.org/abs/2208.08373"},{"text":"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.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"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.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"M. Hahner, C. Sakaridis, M. Bijelic, F. Heide, F. Yu, D. Dai, and L. Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. 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International Conference on Machine Learning (ICML), 2016. arXiv:1506.02142.","identifier":"arXiv:1506.02142","url":"https://arxiv.org/abs/1506.02142"},{"text":"A. N. Angelopoulos and S. Bates. A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification. arXiv:2107.07511, 2021.","identifier":"arXiv:2107.07511","url":"https://arxiv.org/abs/2107.07511"},{"text":"Y. Romano, E. Patterson, and E. J. Candès. Conformalized Quantile Regression. Advances in Neural Information Processing Systems (NeurIPS), 2019. arXiv:1905.03222.","identifier":"arXiv:1905.03222","url":"https://arxiv.org/abs/1905.03222"},{"text":"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.","identifier":"arXiv:2210.10254","url":"https://arxiv.org/abs/2210.10254"},{"text":"G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine. 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