{"kagg_id":"KAGG:2607.00003","slug":"2607.00003","title":"Range-Gated Safety Envelopes for Reactive Quadruped Navigation","authors":[{"name":"Anika Roth","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Selin Aydin","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Lukas Brandt","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"}],"affiliation":null,"abstract":"Reactive navigation layers for legged robots commonly surround obstacles with a fixed inflation margin. Such a margin is conservative at low speed and can be unsafe at high speed, where velocity-controller lag lets the realized motion run past the point at which a command was judged safe. This paper proposes a range-gated safety envelope for reactive quadruped navigation. A configuration-based motion model, following the abstract robot configuration used by AgniNav, predicts the motion realized by each candidate velocity command under a bounded actuation delay, and the predicted free range along the planned direction is evaluated over the planning horizon. A command is admitted only if this predicted range envelope remains inside a speed-dependent corridor whose length and width grow with the worst-case stopping distance; otherwise the layer hands control to a braking maneuver. Under a bounded controller lag and a guaranteed braking deceleration, a certificate-style argument shows that every admitted command preserves the existence of a collision-free stop. The approach is evaluated in an Isaac Lab cluttered-corridor suite with doorway and stair-adjacent segments and on a Unitree Go2 traversing indoor hallways at setpoints from 1.5 to 4.0 m/s. Compared with a fixed inflation margin, the envelope raises collision-free success from 81.4 to 93.2 percent and mean speed from 2.70 to 3.05 m/s while reducing reaction overshoot from 14.2 to 12.8 cm. Ablations indicate that the controller-lag bound and range gating account for most of the gain.","publication_date":"2026-07-17","registered_at":"2026-07-22T08:07:00Z","updated_at":"2026-07-23T14:26:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00003/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"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":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"P. Fiorini and Z. Shiller. Motion Planning in Dynamic Environments Using Velocity Obstacles. International Journal of Robotics Research, 1998.","identifier":"doi:10.1177/027836499801700706","url":"https://doi.org/10.1177/027836499801700706"},{"text":"O. Khatib. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. International Journal of Robotics Research, 1986.","identifier":"doi:10.1177/027836498600500106","url":"https://doi.org/10.1177/027836498600500106"},{"text":"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, 2017. arXiv:1609.06408.","identifier":"arXiv:1609.06408","url":"https://arxiv.org/abs/1609.06408"},{"text":"A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. European Control Conference, 2019. arXiv:1903.11199.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. Robotics: Science and Systems, 2024. arXiv:2401.17583.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"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":"S. Kousik, S. Vaskov, F. Bu, M. Johnson-Roberson, and R. Vasudevan. Bridging the Gap Between Safety and Real-Time Performance in Receding-Horizon Trajectory Design for Mobile Robots. International Journal of Robotics Research, 2020. arXiv:1809.06746.","identifier":"arXiv:1809.06746","url":"https://arxiv.org/abs/1809.06746"},{"text":"A. Majumdar and R. Tedrake. Funnel Libraries for Real-Time Robust Feedback Motion Planning. International Journal of Robotics Research, 2017. arXiv:1601.04037.","identifier":"arXiv:1601.04037","url":"https://arxiv.org/abs/1601.04037"},{"text":"S. L. Herbert, M. Chen, S. Han, S. Bansal, J. F. Fisac, and C. J. Tomlin. FaSTrack: A Modular Framework for Fast and Guaranteed Safe Motion Planning. IEEE Conference on Decision and Control, 2017. arXiv:1703.07373.","identifier":"arXiv:1703.07373","url":"https://arxiv.org/abs/1703.07373"},{"text":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. IEEE Conference on Decision and Control, 2017. arXiv:1709.07523.","identifier":"arXiv:1709.07523","url":"https://arxiv.org/abs/1709.07523"},{"text":"J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin. A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems. IEEE Transactions on Automatic Control, 2019. arXiv:1705.01292.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"text":"K. P. Wabersich and M. N. Zeilinger. Linear Model Predictive Safety Certification for Learning-Based Control. IEEE Conference on Decision and Control, 2018. arXiv:1803.08552.","identifier":"arXiv:1803.08552","url":"https://arxiv.org/abs/1803.08552"},{"text":"K.-C. Hsu, H. Hu, and J. F. Fisac. The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems. Annual Review of Control, Robotics, and Autonomous Systems, 2024. arXiv:2309.05837.","identifier":"arXiv:2309.05837","url":"https://arxiv.org/abs/2309.05837"},{"text":"J. Zeng, B. Zhang, and K. Sreenath. Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function. American Control Conference, 2021. arXiv:2007.11718.","identifier":"arXiv:2007.11718","url":"https://arxiv.org/abs/2007.11718"},{"text":"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, 2021. arXiv:2011.00032.","identifier":"arXiv:2011.00032","url":"https://arxiv.org/abs/2011.00032"},{"text":"T. G. Molnar, R. K. Cosner, A. W. Singletary, W. Ubellacker, and A. D. Ames. Model-Free Safety-Critical Control for Robotic Systems. IEEE Robotics and Automation Letters, 2022. arXiv:2109.09047.","identifier":"arXiv:2109.09047","url":"https://arxiv.org/abs/2109.09047"},{"text":"J. Tordesillas, B. T. Lopez, and J. P. How. FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019. arXiv:1903.03558.","identifier":"arXiv:1903.03558","url":"https://arxiv.org/abs/1903.03558"},{"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":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Mueller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. Science Robotics, 2021. arXiv:2110.05113.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"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":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems, 2021. arXiv:2107.04034.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"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, 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, 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":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. Conference on Robot Learning, 2023. arXiv:2309.05665.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. IEEE International Conference on Robotics and Automation, 2024. arXiv:2309.14341.","identifier":"arXiv:2309.14341","url":"https://arxiv.org/abs/2309.14341"},{"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":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems, 2023. arXiv:2305.08510.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"D. D. Fan, K. Otsu, Y. Kubo, A. Dixit, J. Burdick, and A. Agha-Mohammadi. STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation. Robotics: Science and Systems, 2021. arXiv:2103.02828.","identifier":"arXiv:2103.02828","url":"https://arxiv.org/abs/2103.02828"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics and Automation Letters, 2021. arXiv:2002.05700.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang. FAST-LIO2: Fast Direct LiDAR-Inertial Odometry. IEEE Transactions on Robotics, 2022. arXiv:2107.06829.","identifier":"arXiv:2107.06829","url":"https://arxiv.org/abs/2107.06829"},{"text":"H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017. arXiv:1611.03631.","identifier":"arXiv:1611.03631","url":"https://arxiv.org/abs/1611.03631"},{"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"},{"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":"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. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017. arXiv:1703.06907.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"},{"text":"A. Kadian et al. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? arXiv:1912.06321, 2019.","identifier":"arXiv:1912.06321","url":"https://arxiv.org/abs/1912.06321"},{"text":"N. Yokoyama, S. Ha, and D. Batra. Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation. arXiv:2103.08022, 2021.","identifier":"arXiv:2103.08022","url":"https://arxiv.org/abs/2103.08022"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00003","metadata":{"title":"Range-Gated Safety Envelopes for Reactive Quadruped Navigation","authors":[{"name":"Anika Roth","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Selin Aydin","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Lukas Brandt","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"}],"publication_date":"2026-07-17","abstract":"Reactive navigation layers for legged robots commonly surround obstacles with a fixed inflation margin. Such a margin is conservative at low speed and can be unsafe at high speed, where velocity-controller lag lets the realized motion run past the point at which a command was judged safe. This paper proposes a range-gated safety envelope for reactive quadruped navigation. A configuration-based motion model, following the abstract robot configuration used by AgniNav, predicts the motion realized by each candidate velocity command under a bounded actuation delay, and the predicted free range along the planned direction is evaluated over the planning horizon. A command is admitted only if this predicted range envelope remains inside a speed-dependent corridor whose length and width grow with the worst-case stopping distance; otherwise the layer hands control to a braking maneuver. Under a bounded controller lag and a guaranteed braking deceleration, a certificate-style argument shows that every admitted command preserves the existence of a collision-free stop. The approach is evaluated in an Isaac Lab cluttered-corridor suite with doorway and stair-adjacent segments and on a Unitree Go2 traversing indoor hallways at setpoints from 1.5 to 4.0 m/s. Compared with a fixed inflation margin, the envelope raises collision-free success from 81.4 to 93.2 percent and mean speed from 2.70 to 3.05 m/s while reducing reaction overshoot from 14.2 to 12.8 cm. Ablations indicate that the controller-lag bound and range gating account for most of the gain.","references":[{"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":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"P. Fiorini and Z. Shiller. Motion Planning in Dynamic Environments Using Velocity Obstacles. International Journal of Robotics Research, 1998.","identifier":"doi:10.1177/027836499801700706","url":"https://doi.org/10.1177/027836499801700706"},{"text":"O. Khatib. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. International Journal of Robotics Research, 1986.","identifier":"doi:10.1177/027836498600500106","url":"https://doi.org/10.1177/027836498600500106"},{"text":"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, 2017. arXiv:1609.06408.","identifier":"arXiv:1609.06408","url":"https://arxiv.org/abs/1609.06408"},{"text":"A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada. Control Barrier Functions: Theory and Applications. European Control Conference, 2019. arXiv:1903.11199.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. Robotics: Science and Systems, 2024. arXiv:2401.17583.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"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":"S. Kousik, S. Vaskov, F. Bu, M. Johnson-Roberson, and R. Vasudevan. Bridging the Gap Between Safety and Real-Time Performance in Receding-Horizon Trajectory Design for Mobile Robots. International Journal of Robotics Research, 2020. arXiv:1809.06746.","identifier":"arXiv:1809.06746","url":"https://arxiv.org/abs/1809.06746"},{"text":"A. Majumdar and R. Tedrake. Funnel Libraries for Real-Time Robust Feedback Motion Planning. International Journal of Robotics Research, 2017. arXiv:1601.04037.","identifier":"arXiv:1601.04037","url":"https://arxiv.org/abs/1601.04037"},{"text":"S. L. Herbert, M. Chen, S. Han, S. Bansal, J. F. Fisac, and C. J. Tomlin. FaSTrack: A Modular Framework for Fast and Guaranteed Safe Motion Planning. IEEE Conference on Decision and Control, 2017. arXiv:1703.07373.","identifier":"arXiv:1703.07373","url":"https://arxiv.org/abs/1703.07373"},{"text":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. IEEE Conference on Decision and Control, 2017. arXiv:1709.07523.","identifier":"arXiv:1709.07523","url":"https://arxiv.org/abs/1709.07523"},{"text":"J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin. A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems. IEEE Transactions on Automatic Control, 2019. arXiv:1705.01292.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"text":"K. P. Wabersich and M. N. Zeilinger. Linear Model Predictive Safety Certification for Learning-Based Control. IEEE Conference on Decision and Control, 2018. arXiv:1803.08552.","identifier":"arXiv:1803.08552","url":"https://arxiv.org/abs/1803.08552"},{"text":"K.-C. Hsu, H. Hu, and J. F. Fisac. The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems. Annual Review of Control, Robotics, and Autonomous Systems, 2024. arXiv:2309.05837.","identifier":"arXiv:2309.05837","url":"https://arxiv.org/abs/2309.05837"},{"text":"J. Zeng, B. Zhang, and K. Sreenath. Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function. American Control Conference, 2021. arXiv:2007.11718.","identifier":"arXiv:2007.11718","url":"https://arxiv.org/abs/2007.11718"},{"text":"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, 2021. arXiv:2011.00032.","identifier":"arXiv:2011.00032","url":"https://arxiv.org/abs/2011.00032"},{"text":"T. G. Molnar, R. K. Cosner, A. W. Singletary, W. Ubellacker, and A. D. Ames. Model-Free Safety-Critical Control for Robotic Systems. IEEE Robotics and Automation Letters, 2022. arXiv:2109.09047.","identifier":"arXiv:2109.09047","url":"https://arxiv.org/abs/2109.09047"},{"text":"J. Tordesillas, B. T. Lopez, and J. P. How. FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019. arXiv:1903.03558.","identifier":"arXiv:1903.03558","url":"https://arxiv.org/abs/1903.03558"},{"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":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Mueller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. Science Robotics, 2021. arXiv:2110.05113.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"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":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. Robotics: Science and Systems, 2021. arXiv:2107.04034.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"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, 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, 2022. arXiv:2209.12827.","identifier":"arXiv:2209.12827","url":"https://arxiv.org/abs/2209.12827"},{"text":"D. 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Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. Robotics: Science and Systems, 2023. arXiv:2305.08510.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"D. D. Fan, K. Otsu, Y. Kubo, A. Dixit, J. Burdick, and A. Agha-Mohammadi. STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation. Robotics: Science and Systems, 2021. arXiv:2103.02828.","identifier":"arXiv:2103.02828","url":"https://arxiv.org/abs/2103.02828"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. IEEE Robotics and Automation Letters, 2021. arXiv:2002.05700.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"W. Xu, Y. Cai, D. He, J. Lin, and F. Zhang. FAST-LIO2: Fast Direct LiDAR-Inertial Odometry. 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