{"kagg_id":"KAGG:2608.00007","slug":"2608.00007","title":"How Much Future Range Do You Need? An Ablation Study for Agile Quadruped Navigation","authors":[{"name":"Minju Seo","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"},{"name":"Junho Kang","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"},{"name":"Hyesun Bae","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"}],"affiliation":null,"abstract":"Future-range predictors estimate how far a legged robot can travel along control-aligned directions within a prediction horizon H, but the horizon is usually fixed by hand even though the appropriate look-ahead depends on speed and terrain. In a systematic ablation with the full navigation stack held fixed, the horizon (0.25, 0.5, 1.0, and 2.0 s), the query density, and the number of control-aligned directions are varied for a simulated Unitree Go2 in Isaac Lab across 12 scene families, speeds from 1 to 5 m/s, and flat, mixed, and rough terrain. From these results, a lightweight rule is fitted that selects the horizon from the commanded speed and an online terrain estimate, and the rule is then run on a physical Go2. In the simulated main comparison, the selected rule reaches a 92.3 percent success rate, compared with 81.1 percent for the best fixed horizon and 80.9 percent without future range, while maintaining a mean speed of 3.30 m/s. In the horizon sweep, success peaks at H = 0.5 s and declines for shorter and longer horizons; long horizons are most harmful on occluded turns at 4 m/s, where H = 2.0 s produces 4.7 braking events per km. The rule adds 0.4 ms of decision latency, and on hardware it selected horizons between 0.5 and 1.0 s in 8/8 recorded runs. The fixed-horizon entries are not consistent across the recorded tables, and in the sweep the rule matches but does not exceed the best fixed horizon.","publication_date":"2026-08-14","registered_at":"2026-08-16T10:45:00Z","updated_at":"2026-08-18T15:57:00Z","status":"published","html_path":null,"pdf_path":"articles/2608.00007/paper.pdf","source_type":"preprint","source_label":"Kaggleyes Academy Preprint","license":"CC BY 4.0","has_doi":false,"external_doi":null,"references":[{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"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":"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":"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":"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. 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":"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":"Y. Kim, H. Oh, J. Lee, J. Choi, G. Ji, M. Jung, D. Youm, and J. Hwangbo. Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion. arXiv:2308.12517, 2023.","identifier":"arXiv:2308.12517","url":"https://arxiv.org/abs/2308.12517"},{"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":"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":"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, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. arXiv:2103.04351, 2021.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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":"S. Kareer, N. Yokoyama, D. Batra, S. Ha, and J. Truong. ViNL: Visual Navigation and Locomotion Over Obstacles. arXiv:2210.14791, 2022.","identifier":"arXiv:2210.14791","url":"https://arxiv.org/abs/2210.14791"},{"text":"Y. Kim, C. Kim, and J. Hwangbo. Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation. arXiv:2204.08647, 2022.","identifier":"arXiv:2204.08647","url":"https://arxiv.org/abs/2204.08647"},{"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":"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":"R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter. Perceptive Locomotion through Nonlinear Model Predictive Control. arXiv:2208.08373, 2022.","identifier":"arXiv:2208.08373","url":"https://arxiv.org/abs/2208.08373"},{"text":"D. Hoeller, N. Rudin, C. Choy, A. Anandkumar, and M. Hutter. Neural Scene Representation for Locomotion on Structured Terrain. arXiv:2206.08077, 2022.","identifier":"arXiv:2206.08077","url":"https://arxiv.org/abs/2206.08077"},{"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":"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":"G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine. Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. arXiv:1709.10489, 2017.","identifier":"arXiv:1709.10489","url":"https://arxiv.org/abs/1709.10489"},{"text":"G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou. Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving. arXiv:1707.02342, 2017.","identifier":"arXiv:1707.02342","url":"https://arxiv.org/abs/1707.02342"},{"text":"J. Tordesillas, B. T. Lopez, and J. P. How. FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. arXiv:1903.03558, 2019.","identifier":"arXiv:1903.03558","url":"https://arxiv.org/abs/1903.03558"},{"text":"X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao. EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors. arXiv:2008.08835, 2020.","identifier":"arXiv:2008.08835","url":"https://arxiv.org/abs/2008.08835"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. arXiv:2110.05113, 2021.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"S. Bansal, V. Tolani, S. Gupta, J. Malik, and C. Tomlin. Combining Optimal Control and Learning for Visual Navigation in Novel Environments. arXiv:1903.02531, 2019.","identifier":"arXiv:1903.02531","url":"https://arxiv.org/abs/1903.02531"},{"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":"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":"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":"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":"A. Bouman et al. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion. arXiv:2010.09259, 2020.","identifier":"arXiv:2010.09259","url":"https://arxiv.org/abs/2010.09259"},{"text":"M. Tranzatto et al. CERBERUS in the DARPA Subterranean Challenge. arXiv:2201.07067, 2022.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"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"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00007","metadata":{"title":"How Much Future Range Do You Need? An Ablation Study for Agile Quadruped Navigation","authors":[{"name":"Minju Seo","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"},{"name":"Junho Kang","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"},{"name":"Hyesun Bae","affiliation":"Legged Locomotion and Control Lab, KAIST, Daejeon, South Korea"}],"publication_date":"2026-08-14","abstract":"Future-range predictors estimate how far a legged robot can travel along control-aligned directions within a prediction horizon H, but the horizon is usually fixed by hand even though the appropriate look-ahead depends on speed and terrain. In a systematic ablation with the full navigation stack held fixed, the horizon (0.25, 0.5, 1.0, and 2.0 s), the query density, and the number of control-aligned directions are varied for a simulated Unitree Go2 in Isaac Lab across 12 scene families, speeds from 1 to 5 m/s, and flat, mixed, and rough terrain. From these results, a lightweight rule is fitted that selects the horizon from the commanded speed and an online terrain estimate, and the rule is then run on a physical Go2. In the simulated main comparison, the selected rule reaches a 92.3 percent success rate, compared with 81.1 percent for the best fixed horizon and 80.9 percent without future range, while maintaining a mean speed of 3.30 m/s. In the horizon sweep, success peaks at H = 0.5 s and declines for shorter and longer horizons; long horizons are most harmful on occluded turns at 4 m/s, where H = 2.0 s produces 4.7 braking events per km. The rule adds 0.4 ms of decision latency, and on hardware it selected horizons between 0.5 and 1.0 s in 8/8 recorded runs. The fixed-horizon entries are not consistent across the recorded tables, and in the sweep the rule matches but does not exceed the best fixed horizon.","references":[{"text":"T. Zang, S. Wang, H. Huang, and S. Cheng. FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation. arXiv:2608.01197, 2026.","identifier":"arXiv:2608.01197","url":"https://arxiv.org/abs/2608.01197"},{"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":"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":"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":"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. 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":"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":"Y. Kim, H. Oh, J. Lee, J. Choi, G. Ji, M. Jung, D. Youm, and J. Hwangbo. Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion. arXiv:2308.12517, 2023.","identifier":"arXiv:2308.12517","url":"https://arxiv.org/abs/2308.12517"},{"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":"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":"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, L. Wellhausen, F. Farshidian, and M. Hutter. Learning a State Representation and Navigation in Cluttered and Dynamic Environments. arXiv:2103.04351, 2021.","identifier":"arXiv:2103.04351","url":"https://arxiv.org/abs/2103.04351"},{"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":"S. Kareer, N. Yokoyama, D. Batra, S. Ha, and J. Truong. ViNL: Visual Navigation and Locomotion Over Obstacles. arXiv:2210.14791, 2022.","identifier":"arXiv:2210.14791","url":"https://arxiv.org/abs/2210.14791"},{"text":"Y. Kim, C. Kim, and J. Hwangbo. Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation. arXiv:2204.08647, 2022.","identifier":"arXiv:2204.08647","url":"https://arxiv.org/abs/2204.08647"},{"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":"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":"R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter. Perceptive Locomotion through Nonlinear Model Predictive Control. arXiv:2208.08373, 2022.","identifier":"arXiv:2208.08373","url":"https://arxiv.org/abs/2208.08373"},{"text":"D. Hoeller, N. Rudin, C. Choy, A. Anandkumar, and M. Hutter. Neural Scene Representation for Locomotion on Structured Terrain. arXiv:2206.08077, 2022.","identifier":"arXiv:2206.08077","url":"https://arxiv.org/abs/2206.08077"},{"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":"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":"G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine. Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation. arXiv:1709.10489, 2017.","identifier":"arXiv:1709.10489","url":"https://arxiv.org/abs/1709.10489"},{"text":"G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou. Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving. arXiv:1707.02342, 2017.","identifier":"arXiv:1707.02342","url":"https://arxiv.org/abs/1707.02342"},{"text":"J. Tordesillas, B. T. Lopez, and J. P. How. FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. arXiv:1903.03558, 2019.","identifier":"arXiv:1903.03558","url":"https://arxiv.org/abs/1903.03558"},{"text":"X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao. EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors. arXiv:2008.08835, 2020.","identifier":"arXiv:2008.08835","url":"https://arxiv.org/abs/2008.08835"},{"text":"A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Learning High-Speed Flight in the Wild. arXiv:2110.05113, 2021.","identifier":"arXiv:2110.05113","url":"https://arxiv.org/abs/2110.05113"},{"text":"S. Bansal, V. Tolani, S. Gupta, J. Malik, and C. Tomlin. Combining Optimal Control and Learning for Visual Navigation in Novel Environments. arXiv:1903.02531, 2019.","identifier":"arXiv:1903.02531","url":"https://arxiv.org/abs/1903.02531"},{"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":"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":"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":"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":"A. Bouman et al. Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion. arXiv:2010.09259, 2020.","identifier":"arXiv:2010.09259","url":"https://arxiv.org/abs/2010.09259"},{"text":"M. Tranzatto et al. CERBERUS in the DARPA Subterranean Challenge. arXiv:2201.07067, 2022.","identifier":"arXiv:2201.07067","url":"https://arxiv.org/abs/2201.07067"},{"text":"A. Agha et al. NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge. arXiv:2103.11470, 2021.","identifier":"arXiv:2103.11470","url":"https://arxiv.org/abs/2103.11470"},{"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"}],"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:2608.00007","slug":"2608.00007","registered_at":"2026-08-16T10:45:00Z","updated_at":"2026-08-18T15:57:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2608.00007","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00007","tex_path":"articles/2608.00007/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-08-16T13:02:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2608.00007"}