{"kagg_id":"KAGG:2607.00004","slug":"2607.00004","title":"A Benchmark Protocol for Evaluating Opaque Velocity Interfaces in Legged Robots","authors":[{"name":"Grace Lin","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Wei Xu","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Arjun Mehta","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Ethan Cole","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"}],"affiliation":null,"abstract":"Velocity commands are the de facto interface between navigation software and the locomotion controllers of legged robots, yet these controllers behave as opaque, stateful, and platform-specific systems whose command-to-motion response is rarely documented and never characterized in a common way. This paper proposes a benchmark protocol for evaluating such opaque velocity interfaces. The protocol consists of a fixed suite of 42 probe command sequences drawn from five families (step, sweep, spiral, saturation, and disturbance rejection), five standardized metrics (command fidelity, phase lag, steady-state bias, saturation envelope, and recovery time), and a reporting template that records firmware, gait mode, payload, and terrain alongside the results. GAUGE-style active calibration serves as the reference characterization method. The protocol is demonstrated on two ANYmal B units with identical firmware and different service histories and on one Unitree Go2, on a lab floor and a stair course, with three repetitions per sequence and platform. The characterization produced by the full suite attains a command fidelity error of 8.4 percent RMS and a 95th-percentile phase lag of 62 ms, compared with 9.0 percent and 66 ms for the reference-unit characterization. A probe-budget sweep shows that fidelity error falls from 17.8 to 8.4 percent as the suite grows from 6 to 42 sequences, with diminishing returns beyond 27. The recorded anomalies, including an unmodeled 0.4 s gait-mode transient and payload-dependent early saturation, illustrate behavior that single-unit characterizations miss. The probe suite and exact probe parameters are released with the paper.","publication_date":"2026-07-19","registered_at":"2026-07-21T08:23:00Z","updated_at":"2026-07-23T10:38:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00004/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.","identifier":"","url":""},{"text":"D. Kim, J. Di Carlo, B. Katz, G. Bledt, and S. Kim. Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control. arXiv:1909.06586, 2019.","identifier":"arXiv:1909.06586","url":"https://arxiv.org/abs/1909.06586"},{"text":"C. D. Bellicoso, F. Jenelten, C. Gehring, and M. Hutter. Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots. IEEE Robotics and Automation Letters, 3(3), 2018.","identifier":"","url":""},{"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":"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":"P. Fankhauser, M. Bloesch, and M. Hutter. Probabilistic Terrain Mapping for Mobile Robots with Uncertain Localization. IEEE Robotics and Automation Letters, 3(4), 2018.","identifier":"","url":""},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. arXiv:2303.01420, 2023.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"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":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"","url":""},{"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":"M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Hoepflinger. ANYmal – A Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"","url":""},{"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":"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":"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":"L. Smith, I. Kostrikov, and S. Levine. A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning. arXiv:2208.07860, 2022.","identifier":"arXiv:2208.07860","url":"https://arxiv.org/abs/2208.07860"},{"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":"G. Ji, J. Mun, H. Kim, and J. Hwangbo. Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion. arXiv:2202.05481, 2022.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"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":"S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis. RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control. arXiv:2012.03094, 2020.","identifier":"arXiv:2012.03094","url":"https://arxiv.org/abs/2012.03094"},{"text":"Z. Fu, X. Cheng, and D. Pathak. Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion. arXiv:2210.10044, 2022.","identifier":"arXiv:2210.10044","url":"https://arxiv.org/abs/2210.10044"},{"text":"R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang. Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. arXiv:2107.03996, 2021.","identifier":"arXiv:2107.03996","url":"https://arxiv.org/abs/2107.03996"},{"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":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"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":"M. 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":"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":"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":"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":"L. Ljung. System Identification: Theory for the User, 2nd ed.. Prentice Hall, 1999.","identifier":"","url":""},{"text":"R. Pintelon and J. Schoukens. System Identification: A Frequency Domain Approach, 2nd ed.. Wiley-IEEE Press, 2012.","identifier":"","url":""},{"text":"M. Bloesch, M. Hutter, M. A. Hoepflinger, S. Leutenegger, C. Gehring, C. D. Remy, and R. Siegwart. State Estimation for Legged Robots: Consistent Fusion of Leg Kinematics and IMU. Robotics: Science and Systems (RSS), 2012.","identifier":"","url":""},{"text":"R. Hartley, M. Ghaffari, R. M. Eustice, and J. W. Grizzle. Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation. arXiv:1805.10410, 2018.","identifier":"arXiv:1805.10410","url":"https://arxiv.org/abs/1805.10410"},{"text":"D. Wisth, M. Camurri, and M. Fallon. VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots. arXiv:2107.07243, 2021.","identifier":"arXiv:2107.07243","url":"https://arxiv.org/abs/2107.07243"},{"text":"B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar. Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols. arXiv:1502.03143, 2015.","identifier":"arXiv:1502.03143","url":"https://arxiv.org/abs/1502.03143"},{"text":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"},{"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. 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"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00004","metadata":{"title":"A Benchmark Protocol for Evaluating Opaque Velocity Interfaces in Legged Robots","authors":[{"name":"Grace Lin","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Wei Xu","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Arjun Mehta","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"},{"name":"Ethan Cole","affiliation":"Autonomous Robotics Center, Carnegie Mellon University, Pittsburgh, PA, USA"}],"publication_date":"2026-07-19","abstract":"Velocity commands are the de facto interface between navigation software and the locomotion controllers of legged robots, yet these controllers behave as opaque, stateful, and platform-specific systems whose command-to-motion response is rarely documented and never characterized in a common way. This paper proposes a benchmark protocol for evaluating such opaque velocity interfaces. The protocol consists of a fixed suite of 42 probe command sequences drawn from five families (step, sweep, spiral, saturation, and disturbance rejection), five standardized metrics (command fidelity, phase lag, steady-state bias, saturation envelope, and recovery time), and a reporting template that records firmware, gait mode, payload, and terrain alongside the results. GAUGE-style active calibration serves as the reference characterization method. The protocol is demonstrated on two ANYmal B units with identical firmware and different service histories and on one Unitree Go2, on a lab floor and a stair course, with three repetitions per sequence and platform. The characterization produced by the full suite attains a command fidelity error of 8.4 percent RMS and a 95th-percentile phase lag of 62 ms, compared with 9.0 percent and 66 ms for the reference-unit characterization. A probe-budget sweep shows that fidelity error falls from 17.8 to 8.4 percent as the suite grows from 6 to 42 sequences, with diminishing returns beyond 27. The recorded anomalies, including an unmodeled 0.4 s gait-mode transient and payload-dependent early saturation, illustrate behavior that single-unit characterizations miss. The probe suite and exact probe parameters are released with the paper.","references":[{"text":"J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.","identifier":"","url":""},{"text":"D. Kim, J. Di Carlo, B. Katz, G. Bledt, and S. Kim. Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control. arXiv:1909.06586, 2019.","identifier":"arXiv:1909.06586","url":"https://arxiv.org/abs/1909.06586"},{"text":"C. D. Bellicoso, F. Jenelten, C. Gehring, and M. Hutter. Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots. IEEE Robotics and Automation Letters, 3(3), 2018.","identifier":"","url":""},{"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":"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":"P. Fankhauser, M. Bloesch, and M. Hutter. Probabilistic Terrain Mapping for Mobile Robots with Uncertain Localization. IEEE Robotics and Automation Letters, 3(4), 2018.","identifier":"","url":""},{"text":"L. Wellhausen and M. Hutter. ArtPlanner: Robust Legged Robot Navigation in the Field. arXiv:2303.01420, 2023.","identifier":"arXiv:2303.01420","url":"https://arxiv.org/abs/2303.01420"},{"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":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"","url":""},{"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":"M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. Hoepflinger. ANYmal – A Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"","url":""},{"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":"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":"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":"L. Smith, I. Kostrikov, and S. Levine. A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning. arXiv:2208.07860, 2022.","identifier":"arXiv:2208.07860","url":"https://arxiv.org/abs/2208.07860"},{"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":"G. Ji, J. Mun, H. Kim, and J. Hwangbo. Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion. arXiv:2202.05481, 2022.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"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":"S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis. RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control. arXiv:2012.03094, 2020.","identifier":"arXiv:2012.03094","url":"https://arxiv.org/abs/2012.03094"},{"text":"Z. Fu, X. Cheng, and D. Pathak. Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion. arXiv:2210.10044, 2022.","identifier":"arXiv:2210.10044","url":"https://arxiv.org/abs/2210.10044"},{"text":"R. Yang, M. Zhang, N. Hansen, H. Xu, and X. Wang. Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers. arXiv:2107.03996, 2021.","identifier":"arXiv:2107.03996","url":"https://arxiv.org/abs/2107.03996"},{"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":"Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"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":"M. 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":"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":"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":"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":"L. Ljung. System Identification: Theory for the User, 2nd ed.. Prentice Hall, 1999.","identifier":"","url":""},{"text":"R. Pintelon and J. Schoukens. System Identification: A Frequency Domain Approach, 2nd ed.. Wiley-IEEE Press, 2012.","identifier":"","url":""},{"text":"M. Bloesch, M. Hutter, M. A. Hoepflinger, S. Leutenegger, C. Gehring, C. D. Remy, and R. Siegwart. State Estimation for Legged Robots: Consistent Fusion of Leg Kinematics and IMU. Robotics: Science and Systems (RSS), 2012.","identifier":"","url":""},{"text":"R. Hartley, M. Ghaffari, R. M. Eustice, and J. W. Grizzle. Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation. arXiv:1805.10410, 2018.","identifier":"arXiv:1805.10410","url":"https://arxiv.org/abs/1805.10410"},{"text":"D. Wisth, M. Camurri, and M. Fallon. VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots. arXiv:2107.07243, 2021.","identifier":"arXiv:2107.07243","url":"https://arxiv.org/abs/2107.07243"},{"text":"B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar. Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols. arXiv:1502.03143, 2015.","identifier":"arXiv:1502.03143","url":"https://arxiv.org/abs/1502.03143"},{"text":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"},{"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. 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"}],"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:2607.00004","slug":"2607.00004","registered_at":"2026-07-21T08:23:00Z","updated_at":"2026-07-23T10:38:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00004","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2607.00004","tex_path":"articles/2607.00004/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-07-21T10:40:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2607.00004"}