{"kagg_id":"KAGG:2608.00008","slug":"2608.00008","title":"Probing Opaque Velocity Interfaces on Production Quadruped Platforms: A Field Study","authors":[{"name":"Andrew Foster","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"},{"name":"Lena Vasquez","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"},{"name":"Rajiv Menon","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"}],"affiliation":null,"abstract":"Production quadruped platforms expose velocity command interfaces whose internals (gait managers, safety layers, terrain modes) are undocumented, yet navigation planners routinely assume that commanded velocities are tracked instantly and uniformly. This paper reports a 14-day field characterization of three commercial quadruped platforms, two 70-class research units and one 45-class work unit, across laboratory, campus-path, and light outdoor terrain using GAUGE-style planner-conditioned active calibration. Each platform received 60 probe sessions (20 per terrain mode) logged at 100 Hz, with 0, 5, and 10 kg payloads rotated across sessions. We report command fidelity, 95th-percentile phase lag, saturation-envelope error, and mode-switch transients. On the primary unit with the full probe suite, the command fidelity RMS error is 9.7 percent with a p95 phase lag of 71 ms, compared with 11.1 percent and 79 ms on Platform B and 10.6 percent and 81 ms on Platform C. Restricting probes to the default mode raises the error to 10.5 percent, and removing disturbance rejection raises it to 10.7 percent. Payload is the dominant degradation factor: from 0 to 10 kg, the fidelity error rises from 8.9 to 12.6 percent and the phase lag from 64 to 88 ms. Probes also exposed three behaviors that break naive planning assumptions: an undocumented 3.2 m/s speed limiter in an outdoor mode, a 0.7 s gait-manager hysteresis that produced 0.4 m overshoot, and an unannounced careful mode under a 10 kg payload that halves the usable envelope. We derive interface assumptions that planners for such platforms should state explicitly.","publication_date":"2026-08-18","registered_at":"2026-08-21T08:45:00Z","updated_at":"2026-08-23T17:57:00Z","status":"published","html_path":null,"pdf_path":"articles/2608.00008/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. arXiv:1901.08652, 2019.","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. arXiv:2010.11251, 2020.","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. arXiv:2201.08117, 2022.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"G. Williams, A. Aldrich, and E. Theodorou. Model Predictive Path Integral Control using Covariance Variable Importance Sampling. arXiv:1509.01149, 2015.","identifier":"arXiv:1509.01149","url":"https://arxiv.org/abs/1509.01149"},{"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":"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":"A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn. Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning. arXiv:1803.11347, 2018.","identifier":"arXiv:1803.11347","url":"https://arxiv.org/abs/1803.11347"},{"text":"K. Chua, R. Calandra, R. McAllister, and S. Levine. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. arXiv:1805.12114, 2018.","identifier":"arXiv:1805.12114","url":"https://arxiv.org/abs/1805.12114"},{"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":"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":"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. arXiv:1703.06907, 2017.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"},{"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. 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":"Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg. Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. arXiv:2011.02404, 2020.","identifier":"arXiv:2011.02404","url":"https://arxiv.org/abs/2011.02404"},{"text":"Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox. Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience. arXiv:1810.05687, 2018.","identifier":"arXiv:1810.05687","url":"https://arxiv.org/abs/1810.05687"},{"text":"F. Ramos, R. C. Possas, and D. Fox. BayesSim: Adaptive Domain Randomization via Probabilistic Inference for Robotics Simulators. arXiv:1906.01728, 2019.","identifier":"arXiv:1906.01728","url":"https://arxiv.org/abs/1906.01728"},{"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":"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":"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":"Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots. Fast and Efficient Locomotion via Learned Gait Transitions. arXiv:2104.04644, 2021.","identifier":"arXiv:2104.04644","url":"https://arxiv.org/abs/2104.04644"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"text":"X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023.","identifier":"arXiv:2309.14341","url":"https://arxiv.org/abs/2309.14341"},{"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":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. arXiv:2011.13112, 2020.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"text":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. arXiv:2102.12667, 2021.","identifier":"arXiv:2102.12667","url":"https://arxiv.org/abs/2102.12667"},{"text":"H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas. VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics. arXiv:2203.15983, 2022.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. arXiv:2112.02094, 2021.","identifier":"arXiv:2112.02094","url":"https://arxiv.org/abs/2112.02094"},{"text":"J. Truong, D. Yarats, T. Li, F. Meier, S. Chernova, D. Batra, and A. Rai. Learning Navigation Skills for Legged Robots with Learned Robot Embeddings. arXiv:2011.12255, 2020.","identifier":"arXiv:2011.12255","url":"https://arxiv.org/abs/2011.12255"},{"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. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. arXiv:2204.12876, 2022.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"J. Lee, M. Bjelonic, A. Reske, L. Wellhausen, T. Miki, and M. Hutter. Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots. arXiv:2405.01792, 2024.","identifier":"arXiv:2405.01792","url":"https://arxiv.org/abs/2405.01792"},{"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":"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"},{"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":"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":"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"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00008","metadata":{"title":"Probing Opaque Velocity Interfaces on Production Quadruped Platforms: A Field Study","authors":[{"name":"Andrew Foster","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"},{"name":"Lena Vasquez","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"},{"name":"Rajiv Menon","affiliation":"Advanced Robotics Laboratory, Boston Dynamics, Waltham, MA, USA"}],"publication_date":"2026-08-18","abstract":"Production quadruped platforms expose velocity command interfaces whose internals (gait managers, safety layers, terrain modes) are undocumented, yet navigation planners routinely assume that commanded velocities are tracked instantly and uniformly. This paper reports a 14-day field characterization of three commercial quadruped platforms, two 70-class research units and one 45-class work unit, across laboratory, campus-path, and light outdoor terrain using GAUGE-style planner-conditioned active calibration. Each platform received 60 probe sessions (20 per terrain mode) logged at 100 Hz, with 0, 5, and 10 kg payloads rotated across sessions. We report command fidelity, 95th-percentile phase lag, saturation-envelope error, and mode-switch transients. On the primary unit with the full probe suite, the command fidelity RMS error is 9.7 percent with a p95 phase lag of 71 ms, compared with 11.1 percent and 79 ms on Platform B and 10.6 percent and 81 ms on Platform C. Restricting probes to the default mode raises the error to 10.5 percent, and removing disturbance rejection raises it to 10.7 percent. Payload is the dominant degradation factor: from 0 to 10 kg, the fidelity error rises from 8.9 to 12.6 percent and the phase lag from 64 to 88 ms. Probes also exposed three behaviors that break naive planning assumptions: an undocumented 3.2 m/s speed limiter in an outdoor mode, a 0.7 s gait-manager hysteresis that produced 0.4 m overshoot, and an unannounced careful mode under a 10 kg payload that halves the usable envelope. We derive interface assumptions that planners for such platforms should state explicitly.","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. arXiv:1901.08652, 2019.","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. arXiv:2010.11251, 2020.","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. arXiv:2201.08117, 2022.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"G. Williams, A. Aldrich, and E. Theodorou. Model Predictive Path Integral Control using Covariance Variable Importance Sampling. arXiv:1509.01149, 2015.","identifier":"arXiv:1509.01149","url":"https://arxiv.org/abs/1509.01149"},{"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":"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":"A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn. Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning. arXiv:1803.11347, 2018.","identifier":"arXiv:1803.11347","url":"https://arxiv.org/abs/1803.11347"},{"text":"K. Chua, R. Calandra, R. McAllister, and S. Levine. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. arXiv:1805.12114, 2018.","identifier":"arXiv:1805.12114","url":"https://arxiv.org/abs/1805.12114"},{"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":"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":"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. arXiv:1703.06907, 2017.","identifier":"arXiv:1703.06907","url":"https://arxiv.org/abs/1703.06907"},{"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. 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":"Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg. Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion. arXiv:2011.02404, 2020.","identifier":"arXiv:2011.02404","url":"https://arxiv.org/abs/2011.02404"},{"text":"Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox. Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience. arXiv:1810.05687, 2018.","identifier":"arXiv:1810.05687","url":"https://arxiv.org/abs/1810.05687"},{"text":"F. Ramos, R. C. Possas, and D. Fox. BayesSim: Adaptive Domain Randomization via Probabilistic Inference for Robotics Simulators. arXiv:1906.01728, 2019.","identifier":"arXiv:1906.01728","url":"https://arxiv.org/abs/1906.01728"},{"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":"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":"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":"Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots. Fast and Efficient Locomotion via Learned Gait Transitions. arXiv:2104.04644, 2021.","identifier":"arXiv:2104.04644","url":"https://arxiv.org/abs/2104.04644"},{"text":"A. Agarwal, A. Kumar, J. Malik, and D. Pathak. Legged Locomotion in Challenging Terrains using Egocentric Vision. arXiv:2211.07638, 2022.","identifier":"arXiv:2211.07638","url":"https://arxiv.org/abs/2211.07638"},{"text":"X. Cheng, K. Shi, A. Agarwal, and D. Pathak. Extreme Parkour with Legged Robots. arXiv:2309.14341, 2023.","identifier":"arXiv:2309.14341","url":"https://arxiv.org/abs/2309.14341"},{"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":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. arXiv:2011.13112, 2020.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"text":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. arXiv:2102.12667, 2021.","identifier":"arXiv:2102.12667","url":"https://arxiv.org/abs/2102.12667"},{"text":"H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas. VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics. arXiv:2203.15983, 2022.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"text":"G. Kahn, P. Abbeel, and S. Levine. BADGR: An Autonomous Self-Supervised Learning-Based Navigation System. arXiv:2002.05700, 2020.","identifier":"arXiv:2002.05700","url":"https://arxiv.org/abs/2002.05700"},{"text":"Z. Fu, A. Kumar, A. Agarwal, H. Qi, J. Malik, and D. Pathak. Coupling Vision and Proprioception for Navigation of Legged Robots. arXiv:2112.02094, 2021.","identifier":"arXiv:2112.02094","url":"https://arxiv.org/abs/2112.02094"},{"text":"J. Truong, D. Yarats, T. Li, F. Meier, S. Chernova, D. Batra, and A. Rai. Learning Navigation Skills for Legged Robots with Learned Robot Embeddings. arXiv:2011.12255, 2020.","identifier":"arXiv:2011.12255","url":"https://arxiv.org/abs/2011.12255"},{"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. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter. Elevation Mapping for Locomotion and Navigation using GPU. arXiv:2204.12876, 2022.","identifier":"arXiv:2204.12876","url":"https://arxiv.org/abs/2204.12876"},{"text":"J. Lee, M. Bjelonic, A. Reske, L. Wellhausen, T. Miki, and M. Hutter. Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots. arXiv:2405.01792, 2024.","identifier":"arXiv:2405.01792","url":"https://arxiv.org/abs/2405.01792"},{"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":"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"},{"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":"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":"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"}],"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:2608.00008","slug":"2608.00008","registered_at":"2026-08-21T08:45:00Z","updated_at":"2026-08-23T17:57:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00008","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00008","tex_path":"articles/2608.00008/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-08-21T11:02:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00008"}