{"kagg_id":"KAGG:2608.00004","slug":"2608.00004","title":"Safe Velocity Interface Identification Under Input Saturation","authors":[{"name":"Siyuan Cui","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"},{"name":"Kaiwen Deng","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"},{"name":"Zhenghao Liu","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"}],"affiliation":null,"abstract":"Active calibration of opaque quadruped velocity interfaces, as in GAUGE, issues planner-conditioned probe commands and fits a command-to-motion model online. Informative probes can push the robot toward the saturation boundary of the interface, where the response becomes nonlinear and potentially unsafe on rough ground. This paper adds a probe safety layer: each probe is admitted only if it keeps the robot inside a verified reachable-and-recoverable region computed from a conservative inner model, namely a linear command-response model valid below a certified saturation bound with uncertainty from a recursive least-squares confidence set. Probes are selected to maximize planner-weighted information subject to saturation, excursion, and recovery-time constraints, and the safety margin is derived from the same confidence set as the identification estimate, so the safe region expands as confidence grows. Under bounded-disturbance and inner-linearity assumptions, the analysis shows that the certified identification residual and the safety margin evolve jointly: the margin is nonincreasing, safety holds with high probability, and both quantities decay at the same rate under persistent excitation. In 120 simulated identification sessions on a Unitree Go2 in a cluttered course with an 8-degree slope, the method reached a 10.8 percent RMS identification residual versus 11.3 percent for unconstrained GAUGE-style probes, reduced the mean maximum excursion outside the safe region from 5.4 cm to 4.7 cm, and reached navigation-grade accuracy in 52 s instead of 60 s with smaller probes. In ten hardware sessions, a runtime monitor truncated one probe after a 0.04 m/s violation of the certified saturation bound.","publication_date":"2026-08-07","registered_at":"2026-08-14T11:23:00Z","updated_at":"2026-08-16T13:38:00Z","status":"published","html_path":null,"pdf_path":"articles/2608.00004/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. arXiv:2010.11251, 2020.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"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.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"T. Zang, H. Huang, S. Cheng, and W. Zhang. GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces. arXiv:2607.02841, 2026.","identifier":"arXiv:2607.02841","url":"https://arxiv.org/abs/2607.02841"},{"text":"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.","identifier":"arXiv:1702.02453","url":"https://arxiv.org/abs/1702.02453"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"I. M. A. Nahrendra, B. Yu, and H. Myung. DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning. arXiv:2301.10602, 2023.","identifier":"arXiv:2301.10602","url":"https://arxiv.org/abs/2301.10602"},{"text":"G. B. Margolis and P. Agrawal. Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. arXiv:2212.03238, 2022.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"M. O'Connell, G. Shi, X. Shi, K. Azizzadenesheli, A. Anandkumar, Y. Yue, and S.-J. Chung. Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds. arXiv:2205.06908, 2022.","identifier":"arXiv:2205.06908","url":"https://arxiv.org/abs/2205.06908"},{"text":"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.","identifier":"arXiv:1804.10332","url":"https://arxiv.org/abs/1804.10332"},{"text":"X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv:1710.06537, 2017.","identifier":"arXiv:1710.06537","url":"https://arxiv.org/abs/1710.06537"},{"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. arXiv:1901.08652, 2019.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","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. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022.","identifier":"arXiv:2205.02824","url":"https://arxiv.org/abs/2205.02824"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. arXiv:2209.12827, 2022.","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. arXiv:2306.14874, 2023.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. arXiv:2305.08510, 2023.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"T. M. Moldovan and P. Abbeel. Safe Exploration in Markov Decision Processes. arXiv:1205.4810, 2012.","identifier":"arXiv:1205.4810","url":"https://arxiv.org/abs/1205.4810"},{"text":"M. Turchetta, F. Berkenkamp, and A. Krause. Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. arXiv:1606.04753, 2016.","identifier":"arXiv:1606.04753","url":"https://arxiv.org/abs/1606.04753"},{"text":"F. Berkenkamp, M. Turchetta, A. P. Schoellig, and A. Krause. Safe Model-based Reinforcement Learning with Stability Guarantees. arXiv:1705.08551, 2017.","identifier":"arXiv:1705.08551","url":"https://arxiv.org/abs/1705.08551"},{"text":"T. Koller, F. Berkenkamp, M. Turchetta, and A. Krause. Learning-based Model Predictive Control for Safe Exploration. arXiv:1803.08287, 2018.","identifier":"arXiv:1803.08287","url":"https://arxiv.org/abs/1803.08287"},{"text":"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.","identifier":"arXiv:2008.11700","url":"https://arxiv.org/abs/2008.11700"},{"text":"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.","identifier":"arXiv:2108.06266","url":"https://arxiv.org/abs/2108.06266"},{"text":"A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada. Control Barrier Function Based Quadratic Programs for Safety Critical Systems. arXiv:1609.06408, 2016.","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. arXiv:1903.11199, 2019.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"A. J. Taylor, A. Singletary, Y. Yue, and A. D. Ames. Learning for Safety-Critical Control with Control Barrier Functions. arXiv:1912.10099, 2019.","identifier":"arXiv:1912.10099","url":"https://arxiv.org/abs/1912.10099"},{"text":"R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. arXiv:1903.08792, 2019.","identifier":"arXiv:1903.08792","url":"https://arxiv.org/abs/1903.08792"},{"text":"J. Zeng, B. Zhang, and K. Sreenath. Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function. arXiv:2007.11718, 2020.","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. arXiv:2011.00032, 2020.","identifier":"arXiv:2011.00032","url":"https://arxiv.org/abs/2011.00032"},{"text":"K. P. Wabersich and M. N. Zeilinger. Linear Model Predictive Safety Certification for Learning-Based Control. arXiv:1803.08552, 2018.","identifier":"arXiv:1803.08552","url":"https://arxiv.org/abs/1803.08552"},{"text":"K. P. Wabersich and M. N. Zeilinger. A Predictive Safety Filter for Learning-Based Control of Constrained Nonlinear Dynamical Systems. arXiv:1812.05506, 2018.","identifier":"arXiv:1812.05506","url":"https://arxiv.org/abs/1812.05506"},{"text":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. arXiv:1709.07523, 2017.","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. arXiv:1705.01292, 2017.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"text":"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.","identifier":"arXiv:2309.05837","url":"https://arxiv.org/abs/2309.05837"},{"text":"M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu. Safe Reinforcement Learning via Shielding. arXiv:1708.08611, 2017.","identifier":"arXiv:1708.08611","url":"https://arxiv.org/abs/1708.08611"},{"text":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. arXiv:2401.17583, 2024.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"text":"T.-Y. Yang, T. Zhang, L. Luu, S. Ha, J. Tan, and W. Yu. Safe Reinforcement Learning for Legged Locomotion. arXiv:2203.02638, 2022.","identifier":"arXiv:2203.02638","url":"https://arxiv.org/abs/2203.02638"},{"text":"S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu. On the Sample Complexity of the Linear Quadratic Regulator. arXiv:1710.01688, 2017.","identifier":"arXiv:1710.01688","url":"https://arxiv.org/abs/1710.01688"},{"text":"M. Simchowitz, H. Mania, S. Tu, M. I. Jordan, and B. Recht. Learning Without Mixing: Towards a Sharp Analysis of Linear System Identification. arXiv:1802.08334, 2018.","identifier":"arXiv:1802.08334","url":"https://arxiv.org/abs/1802.08334"},{"text":"A. Wagenmaker and K. Jamieson. Active Learning for Identification of Linear Dynamical Systems. arXiv:2002.00495, 2020.","identifier":"arXiv:2002.00495","url":"https://arxiv.org/abs/2002.00495"},{"text":"H. Mania, M. I. Jordan, and B. Recht. Active Learning for Nonlinear System Identification with Guarantees. arXiv:2006.10277, 2020.","identifier":"arXiv:2006.10277","url":"https://arxiv.org/abs/2006.10277"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00004","metadata":{"title":"Safe Velocity Interface Identification Under Input Saturation","authors":[{"name":"Siyuan Cui","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"},{"name":"Kaiwen Deng","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"},{"name":"Zhenghao Liu","affiliation":"Autonomous Systems Center, National University of Defense Technology, Changsha, China"}],"publication_date":"2026-08-07","abstract":"Active calibration of opaque quadruped velocity interfaces, as in GAUGE, issues planner-conditioned probe commands and fits a command-to-motion model online. Informative probes can push the robot toward the saturation boundary of the interface, where the response becomes nonlinear and potentially unsafe on rough ground. This paper adds a probe safety layer: each probe is admitted only if it keeps the robot inside a verified reachable-and-recoverable region computed from a conservative inner model, namely a linear command-response model valid below a certified saturation bound with uncertainty from a recursive least-squares confidence set. Probes are selected to maximize planner-weighted information subject to saturation, excursion, and recovery-time constraints, and the safety margin is derived from the same confidence set as the identification estimate, so the safe region expands as confidence grows. Under bounded-disturbance and inner-linearity assumptions, the analysis shows that the certified identification residual and the safety margin evolve jointly: the margin is nonincreasing, safety holds with high probability, and both quantities decay at the same rate under persistent excitation. In 120 simulated identification sessions on a Unitree Go2 in a cluttered course with an 8-degree slope, the method reached a 10.8 percent RMS identification residual versus 11.3 percent for unconstrained GAUGE-style probes, reduced the mean maximum excursion outside the safe region from 5.4 cm to 4.7 cm, and reached navigation-grade accuracy in 52 s instead of 60 s with smaller probes. In ten hardware sessions, a runtime monitor truncated one probe after a 0.04 m/s violation of the certified saturation bound.","references":[{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. arXiv:2010.11251, 2020.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"N. Rudin, D. Hoeller, P. Reist, and M. Hutter. Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning. arXiv:2109.11978, 2021.","identifier":"arXiv:2109.11978","url":"https://arxiv.org/abs/2109.11978"},{"text":"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.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"T. Zang, H. Huang, S. Cheng, and W. Zhang. GAUGE: Planner-Conditioned Active Calibration of Opaque Quadruped Velocity Interfaces. arXiv:2607.02841, 2026.","identifier":"arXiv:2607.02841","url":"https://arxiv.org/abs/2607.02841"},{"text":"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.","identifier":"arXiv:1702.02453","url":"https://arxiv.org/abs/1702.02453"},{"text":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"I. M. A. Nahrendra, B. Yu, and H. Myung. DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning. arXiv:2301.10602, 2023.","identifier":"arXiv:2301.10602","url":"https://arxiv.org/abs/2301.10602"},{"text":"G. B. Margolis and P. Agrawal. Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior. arXiv:2212.03238, 2022.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"M. O'Connell, G. Shi, X. Shi, K. Azizzadenesheli, A. Anandkumar, Y. Yue, and S.-J. Chung. Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds. arXiv:2205.06908, 2022.","identifier":"arXiv:2205.06908","url":"https://arxiv.org/abs/2205.06908"},{"text":"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.","identifier":"arXiv:1804.10332","url":"https://arxiv.org/abs/1804.10332"},{"text":"X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. arXiv:1710.06537, 2017.","identifier":"arXiv:1710.06537","url":"https://arxiv.org/abs/1710.06537"},{"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. arXiv:1901.08652, 2019.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","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. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. arXiv:2205.02824, 2022.","identifier":"arXiv:2205.02824","url":"https://arxiv.org/abs/2205.02824"},{"text":"N. Rudin, D. Hoeller, M. Bjelonic, and M. Hutter. Advanced Skills by Learning Locomotion and Local Navigation End-to-End. arXiv:2209.12827, 2022.","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. arXiv:2306.14874, 2023.","identifier":"arXiv:2306.14874","url":"https://arxiv.org/abs/2306.14874"},{"text":"J. Frey, M. Mattamala, N. Chebrolu, C. Cadena, M. Fallon, and M. Hutter. Fast Traversability Estimation for Wild Visual Navigation. arXiv:2305.08510, 2023.","identifier":"arXiv:2305.08510","url":"https://arxiv.org/abs/2305.08510"},{"text":"T. M. Moldovan and P. Abbeel. Safe Exploration in Markov Decision Processes. arXiv:1205.4810, 2012.","identifier":"arXiv:1205.4810","url":"https://arxiv.org/abs/1205.4810"},{"text":"M. Turchetta, F. Berkenkamp, and A. Krause. Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. arXiv:1606.04753, 2016.","identifier":"arXiv:1606.04753","url":"https://arxiv.org/abs/1606.04753"},{"text":"F. Berkenkamp, M. Turchetta, A. P. Schoellig, and A. Krause. Safe Model-based Reinforcement Learning with Stability Guarantees. arXiv:1705.08551, 2017.","identifier":"arXiv:1705.08551","url":"https://arxiv.org/abs/1705.08551"},{"text":"T. Koller, F. Berkenkamp, M. Turchetta, and A. Krause. Learning-based Model Predictive Control for Safe Exploration. arXiv:1803.08287, 2018.","identifier":"arXiv:1803.08287","url":"https://arxiv.org/abs/1803.08287"},{"text":"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.","identifier":"arXiv:2008.11700","url":"https://arxiv.org/abs/2008.11700"},{"text":"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.","identifier":"arXiv:2108.06266","url":"https://arxiv.org/abs/2108.06266"},{"text":"A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada. Control Barrier Function Based Quadratic Programs for Safety Critical Systems. arXiv:1609.06408, 2016.","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. arXiv:1903.11199, 2019.","identifier":"arXiv:1903.11199","url":"https://arxiv.org/abs/1903.11199"},{"text":"A. J. Taylor, A. Singletary, Y. Yue, and A. D. Ames. Learning for Safety-Critical Control with Control Barrier Functions. arXiv:1912.10099, 2019.","identifier":"arXiv:1912.10099","url":"https://arxiv.org/abs/1912.10099"},{"text":"R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks. arXiv:1903.08792, 2019.","identifier":"arXiv:1903.08792","url":"https://arxiv.org/abs/1903.08792"},{"text":"J. Zeng, B. Zhang, and K. Sreenath. Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function. arXiv:2007.11718, 2020.","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. arXiv:2011.00032, 2020.","identifier":"arXiv:2011.00032","url":"https://arxiv.org/abs/2011.00032"},{"text":"K. P. Wabersich and M. N. Zeilinger. Linear Model Predictive Safety Certification for Learning-Based Control. arXiv:1803.08552, 2018.","identifier":"arXiv:1803.08552","url":"https://arxiv.org/abs/1803.08552"},{"text":"K. P. Wabersich and M. N. Zeilinger. A Predictive Safety Filter for Learning-Based Control of Constrained Nonlinear Dynamical Systems. arXiv:1812.05506, 2018.","identifier":"arXiv:1812.05506","url":"https://arxiv.org/abs/1812.05506"},{"text":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. arXiv:1709.07523, 2017.","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. arXiv:1705.01292, 2017.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"text":"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.","identifier":"arXiv:2309.05837","url":"https://arxiv.org/abs/2309.05837"},{"text":"M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu. Safe Reinforcement Learning via Shielding. arXiv:1708.08611, 2017.","identifier":"arXiv:1708.08611","url":"https://arxiv.org/abs/1708.08611"},{"text":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. arXiv:2401.17583, 2024.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"text":"T.-Y. Yang, T. Zhang, L. Luu, S. Ha, J. Tan, and W. Yu. Safe Reinforcement Learning for Legged Locomotion. arXiv:2203.02638, 2022.","identifier":"arXiv:2203.02638","url":"https://arxiv.org/abs/2203.02638"},{"text":"S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu. On the Sample Complexity of the Linear Quadratic Regulator. arXiv:1710.01688, 2017.","identifier":"arXiv:1710.01688","url":"https://arxiv.org/abs/1710.01688"},{"text":"M. Simchowitz, H. Mania, S. Tu, M. I. Jordan, and B. Recht. Learning Without Mixing: Towards a Sharp Analysis of Linear System Identification. arXiv:1802.08334, 2018.","identifier":"arXiv:1802.08334","url":"https://arxiv.org/abs/1802.08334"},{"text":"A. Wagenmaker and K. Jamieson. Active Learning for Identification of Linear Dynamical Systems. arXiv:2002.00495, 2020.","identifier":"arXiv:2002.00495","url":"https://arxiv.org/abs/2002.00495"},{"text":"H. Mania, M. I. Jordan, and B. Recht. Active Learning for Nonlinear System Identification with Guarantees. arXiv:2006.10277, 2020.","identifier":"arXiv:2006.10277","url":"https://arxiv.org/abs/2006.10277"}],"affiliation":null,"source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"status":"published","kagg_id":"KAGG:2608.00004","slug":"2608.00004","registered_at":"2026-08-14T11:23:00Z","updated_at":"2026-08-16T13:38:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00004","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00004","tex_path":"articles/2608.00004/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-08-14T13:40:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00004"}