{"kagg_id":"KAGG:2607.00002","slug":"2607.00002","title":"Terrain-Aware Velocity Command Smoothing for Quadruped Navigation over Unstructured Ground","authors":[{"name":"Tomas Keller","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Priya Raghunathan","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Hana Sato","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"}],"affiliation":null,"abstract":"Local planners for legged robots emit velocity commands without regard for what the locomotion controller can smoothly realize. On unstructured ground this mismatch produces command chatter, degrades tracking, wastes energy, and triggers premature obstacle reactions. This paper presents a terrain-aware, differentiable command-smoothing layer placed between a configuration-driven local planner in the AgniNav style and an opaque quadruped velocity interface. The layer solves a short-horizon smoothing problem whose weights are scheduled by an online terrain-difficulty estimate, and it predicts realized motion with an interface model that is initialized by GAUGE-style active calibration and updated online. Its parameters are trained end-to-end in simulation through implicit differentiation, and its prediction of the realizable motion is fed back to the planner so that collision checking follows the motion the robot is expected to execute. The approach is evaluated in an Isaac Lab deformable-terrain suite with five terrain classes and in field trials with a Unitree B2 over gravel, exposed roots, and graded slopes at 1.0 to 2.5 m/s. Compared with unsmoothed planner output, it reduces command RMS from 9.8 to 9.1 cm/s and 95th-percentile tracking error from 14.7 to 14.2 cm, raises success rate from 86.0 to 91.5 percent, lowers an energy proxy from 1519 to 1385 J/km, and increases mean speed from 1.48 to 1.68 m/s. It also outperforms a fixed gain scheduler and a GAUGE-calibrated fixed-gain smoother. Ablations and recorded failure cases show which components matter and where the approach remains fragile.","publication_date":"2026-07-16","registered_at":"2026-07-18T15:23:00Z","updated_at":"2026-07-18T18:44:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00002/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, 2019. arXiv:1901.08652.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, 2022. arXiv:2201.08117.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"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":"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":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. Autonomous Robots, 2022. arXiv:2011.13112.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"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":"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":"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. IEEE Robotics and Automation Letters, 2022. arXiv:2202.05481.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. Robotics: Science and Systems, 2022. arXiv:2205.02824.","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. Conference on Robot Learning, 2022. arXiv:2212.03238.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"Z. Fu, A. Kumar, J. Malik, and D. Pathak. Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots. Conference on Robot Learning, 2021. arXiv:2111.01674.","identifier":"arXiv:2111.01674","url":"https://arxiv.org/abs/2111.01674"},{"text":"Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots. Fast and Efficient Locomotion via Learned Gait Transitions. Conference on Robot Learning, 2021. arXiv:2104.04644.","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. Conference on Robot Learning, 2022. arXiv:2211.07638.","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. IEEE International Conference on Robotics and Automation, 2024. arXiv:2309.14341.","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. Conference on Robot Learning, 2023. arXiv:2309.05665.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"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":"S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis. RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control. IEEE Transactions on Robotics, 2022. arXiv:2012.03094.","identifier":"arXiv:2012.03094","url":"https://arxiv.org/abs/2012.03094"},{"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, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, 2021. arXiv:2103.04351.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021. arXiv:2011.12255.","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. 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":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. IEEE International Conference on Robotics and Automation, 2023. arXiv:2210.03370.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"G. Williams, A. Aldrich, and E. A. 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":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. IEEE Robotics and Automation Letters, 2021. arXiv:2102.12667.","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. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022. arXiv:2203.15983.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"text":"B. Amos and J. Z. Kolter. OptNet: Differentiable Optimization as a Layer in Neural Networks. International Conference on Machine Learning, 2017. arXiv:1703.00443.","identifier":"arXiv:1703.00443","url":"https://arxiv.org/abs/1703.00443"},{"text":"B. Amos, I. D. J. Rodriguez, J. Sacks, B. Boots, and J. Z. Kolter. Differentiable MPC for End-to-end Planning and Control. Advances in Neural Information Processing Systems, 2018. arXiv:1810.13400.","identifier":"arXiv:1810.13400","url":"https://arxiv.org/abs/1810.13400"},{"text":"A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter. Differentiable Convex Optimization Layers. Advances in Neural Information Processing Systems, 2019. arXiv:1910.12430.","identifier":"arXiv:1910.12430","url":"https://arxiv.org/abs/1910.12430"},{"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":"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":"F. Berkenkamp, M. Turchetta, A. P. Schoellig, and A. Krause. Safe Model-based Reinforcement Learning with Stability Guarantees. Advances in Neural Information Processing Systems, 2017. arXiv:1705.08551.","identifier":"arXiv:1705.08551","url":"https://arxiv.org/abs/1705.08551"},{"text":"V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. NeurIPS Datasets and Benchmarks Track, 2021. arXiv:2108.10470.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg. 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":"J. 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Chernova, and D. Batra. Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? IEEE Robotics and Automation Letters, 2020. arXiv:1912.06321.","identifier":"arXiv:1912.06321","url":"https://arxiv.org/abs/1912.06321"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00002","metadata":{"title":"Terrain-Aware Velocity Command Smoothing for Quadruped Navigation over Unstructured Ground","authors":[{"name":"Tomas Keller","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Priya Raghunathan","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Hana Sato","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"}],"publication_date":"2026-07-16","abstract":"Local planners for legged robots emit velocity commands without regard for what the locomotion controller can smoothly realize. On unstructured ground this mismatch produces command chatter, degrades tracking, wastes energy, and triggers premature obstacle reactions. This paper presents a terrain-aware, differentiable command-smoothing layer placed between a configuration-driven local planner in the AgniNav style and an opaque quadruped velocity interface. The layer solves a short-horizon smoothing problem whose weights are scheduled by an online terrain-difficulty estimate, and it predicts realized motion with an interface model that is initialized by GAUGE-style active calibration and updated online. Its parameters are trained end-to-end in simulation through implicit differentiation, and its prediction of the realizable motion is fed back to the planner so that collision checking follows the motion the robot is expected to execute. The approach is evaluated in an Isaac Lab deformable-terrain suite with five terrain classes and in field trials with a Unitree B2 over gravel, exposed roots, and graded slopes at 1.0 to 2.5 m/s. Compared with unsmoothed planner output, it reduces command RMS from 9.8 to 9.1 cm/s and 95th-percentile tracking error from 14.7 to 14.2 cm, raises success rate from 86.0 to 91.5 percent, lowers an energy proxy from 1519 to 1385 J/km, and increases mean speed from 1.48 to 1.68 m/s. It also outperforms a fixed gain scheduler and a GAUGE-calibrated fixed-gain smoother. Ablations and recorded failure cases show which components matter and where the approach remains fragile.","references":[{"text":"J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter. Learning Agile and Dynamic Motor Skills for Legged Robots. Science Robotics, 2019. arXiv:1901.08652.","identifier":"arXiv:1901.08652","url":"https://arxiv.org/abs/1901.08652"},{"text":"J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Quadrupedal Locomotion over Challenging Terrain. Science Robotics, 2020. arXiv:2010.11251.","identifier":"arXiv:2010.11251","url":"https://arxiv.org/abs/2010.11251"},{"text":"T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter. Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild. Science Robotics, 2022. arXiv:2201.08117.","identifier":"arXiv:2201.08117","url":"https://arxiv.org/abs/2201.08117"},{"text":"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":"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":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. Autonomous Robots, 2022. arXiv:2011.13112.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"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":"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":"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. IEEE Robotics and Automation Letters, 2022. arXiv:2202.05481.","identifier":"arXiv:2202.05481","url":"https://arxiv.org/abs/2202.05481"},{"text":"G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal. Rapid Locomotion via Reinforcement Learning. Robotics: Science and Systems, 2022. arXiv:2205.02824.","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. Conference on Robot Learning, 2022. arXiv:2212.03238.","identifier":"arXiv:2212.03238","url":"https://arxiv.org/abs/2212.03238"},{"text":"Z. Fu, A. Kumar, J. Malik, and D. Pathak. Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots. Conference on Robot Learning, 2021. arXiv:2111.01674.","identifier":"arXiv:2111.01674","url":"https://arxiv.org/abs/2111.01674"},{"text":"Y. Yang, T. Zhang, E. Coumans, J. Tan, and B. Boots. Fast and Efficient Locomotion via Learned Gait Transitions. Conference on Robot Learning, 2021. arXiv:2104.04644.","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. Conference on Robot Learning, 2022. arXiv:2211.07638.","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. IEEE International Conference on Robotics and Automation, 2024. arXiv:2309.14341.","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. Conference on Robot Learning, 2023. arXiv:2309.05665.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"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":"S. Gangapurwala, M. Geisert, R. Orsolino, M. Fallon, and I. Havoutis. RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control. IEEE Transactions on Robotics, 2022. arXiv:2012.03094.","identifier":"arXiv:2012.03094","url":"https://arxiv.org/abs/2012.03094"},{"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, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. IEEE Robotics and Automation Letters, 2021. arXiv:2103.04351.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021. arXiv:2011.12255.","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. 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":"D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine. GNM: A General Navigation Model to Drive Any Robot. IEEE International Conference on Robotics and Automation, 2023. arXiv:2210.03370.","identifier":"arXiv:2210.03370","url":"https://arxiv.org/abs/2210.03370"},{"text":"G. Williams, A. Aldrich, and E. A. 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":"X. Xiao, J. Biswas, and P. Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. IEEE Robotics and Automation Letters, 2021. arXiv:2102.12667.","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. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022. arXiv:2203.15983.","identifier":"arXiv:2203.15983","url":"https://arxiv.org/abs/2203.15983"},{"text":"B. Amos and J. Z. Kolter. OptNet: Differentiable Optimization as a Layer in Neural Networks. International Conference on Machine Learning, 2017. arXiv:1703.00443.","identifier":"arXiv:1703.00443","url":"https://arxiv.org/abs/1703.00443"},{"text":"B. Amos, I. D. J. Rodriguez, J. Sacks, B. Boots, and J. Z. Kolter. Differentiable MPC for End-to-end Planning and Control. 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