{"kagg_id":"KAGG:2607.00005","slug":"2607.00005","title":"Cross-Embodiment Distillation of Local Navigation Planners from Humanoid to Quadruped Teams","authors":[{"name":"Yifan Chen","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Zhiyuan Wang","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Haoran Liu","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Xiaotong Zhang","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"}],"affiliation":null,"abstract":"Configuration-driven local planners such as AgniNav can serve several robot embodiments from a single planning stack, but the full planner is too expensive for the onboard computers of commodity quadrupeds. This preprint presents a cross-embodiment distillation pipeline in which a configuration-driven teacher planner generates local navigation trajectories in a shared, body-normalized configuration space, and a compact onboard student policy is distilled from these trajectories with an explicit embodiment descriptor as input. Training combines on-policy data aggregation, a finite-difference consistency regularizer that aligns the sensitivity of the student to descriptor changes with that of the teacher, and a held-out embodiment split used for model selection. The pipeline is evaluated in Isaac Lab with a Unitree Go2 and a 12-DoF H1-class humanoid over 20 indoor-outdoor transition scenes, 5 seeds, and 50 episodes per scene and seed, and onboard on a Go2 whose Jetson Orin computer runs the student. The distilled student reaches 90.4 percent navigation success, compared with 81.5 percent for a per-embodiment oracle teacher and 82.3 percent for a student without embodiment conditioning, with a teacher parity score of 93.1 percent and an onboard decision latency of 8.2 ms. Its mean path clearance margin of 18.7 cm is, however, the smallest among the compared methods. Ablations show drops of 11.3 to 15.3 percentage points when the descriptor input, the consistency regularizer, or the held-out split is removed. Recorded failures include reduced clearance on a steep entry ramp and degraded success under descriptor dropout.","publication_date":"2026-07-21","registered_at":"2026-07-25T10:45:00Z","updated_at":"2026-07-25T14:44:00Z","status":"published","html_path":null,"pdf_path":"articles/2607.00005/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. 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":"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":"J. Zhang, Y. Du, X. Guo, S. Sun, X. Liu, Y. Sun, G. Lu, W. Sui, and J. Li. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness. arXiv:2607.19880, 2026.","identifier":"arXiv:2607.19880","url":"https://arxiv.org/abs/2607.19880"},{"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":"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":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"A. Eftekhar et al.. The One RING: a Robotic Indoor Navigation Generalist. arXiv:2412.14401, 2024.","identifier":"arXiv:2412.14401","url":"https://arxiv.org/abs/2412.14401"},{"text":"J. Zhang et al.. Embodied Navigation Foundation Model. arXiv:2509.12129, 2025.","identifier":"arXiv:2509.12129","url":"https://arxiv.org/abs/2509.12129"},{"text":"J. Yang, C. Glossop, A. Bhorkar, D. Shah, Q. Vuong, C. Finn, D. Sadigh, and S. Levine. Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation. arXiv:2402.19432, 2024.","identifier":"arXiv:2402.19432","url":"https://arxiv.org/abs/2402.19432"},{"text":"R. Doshi, H. Walke, O. Mees, S. Dasari, and S. Levine. Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation. arXiv:2408.11812, 2024.","identifier":"arXiv:2408.11812","url":"https://arxiv.org/abs/2408.11812"},{"text":"Open X-Embodiment Collaboration. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv:2310.08864, 2023.","identifier":"arXiv:2310.08864","url":"https://arxiv.org/abs/2310.08864"},{"text":"T. Chen, A. Murali, and A. Gupta. Hardware Conditioned Policies for Multi-Robot Transfer Learning. arXiv:1811.09864, 2018.","identifier":"arXiv:1811.09864","url":"https://arxiv.org/abs/1811.09864"},{"text":"W. Huang, I. Mordatch, and D. Pathak. One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. arXiv:2007.04976, 2020.","identifier":"arXiv:2007.04976","url":"https://arxiv.org/abs/2007.04976"},{"text":"A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei. MetaMorph: Learning Universal Controllers with Transformers. arXiv:2203.11931, 2022.","identifier":"arXiv:2203.11931","url":"https://arxiv.org/abs/2203.11931"},{"text":"G. Feng et al.. GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots. arXiv:2209.05309, 2022.","identifier":"arXiv:2209.05309","url":"https://arxiv.org/abs/2209.05309"},{"text":"M. Shafiee, G. Bellegarda, and A. Ijspeert. ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots. arXiv:2310.10486, 2023.","identifier":"arXiv:2310.10486","url":"https://arxiv.org/abs/2310.10486"},{"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. Hinton, O. Vinyals, and J. Dean. Distilling the Knowledge in a Neural Network. arXiv:1503.02531, 2015.","identifier":"arXiv:1503.02531","url":"https://arxiv.org/abs/1503.02531"},{"text":"A. A. Rusu et al.. Policy Distillation. arXiv:1511.06295, 2015.","identifier":"arXiv:1511.06295","url":"https://arxiv.org/abs/1511.06295"},{"text":"S. Ross, G. J. Gordon, and J. A. Bagnell. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. arXiv:1011.0686, 2010.","identifier":"arXiv:1011.0686","url":"https://arxiv.org/abs/1011.0686"},{"text":"Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. Theodorou, and B. Boots. Agile Autonomous Driving using End-to-End Deep Imitation Learning. arXiv:1709.07174, 2017.","identifier":"arXiv:1709.07174","url":"https://arxiv.org/abs/1709.07174"},{"text":"D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl. Learning by Cheating. arXiv:1912.12294, 2019.","identifier":"arXiv:1912.12294","url":"https://arxiv.org/abs/1912.12294"},{"text":"E. Kaufmann, A. Loquercio, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Deep Drone Acrobatics. arXiv:2006.05768, 2020.","identifier":"arXiv:2006.05768","url":"https://arxiv.org/abs/2006.05768"},{"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":"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":"W. M. Czarnecki, S. Osindero, M. Jaderberg, G. Swirszcz, and R. Pascanu. Sobolev Training for Neural Networks. arXiv:1706.04859, 2017.","identifier":"arXiv:1706.04859","url":"https://arxiv.org/abs/1706.04859"},{"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":"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":"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. 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 et al.. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"F. Jenelten, J. He, F. Farshidian, and M. Hutter. DTC: Deep Tracking Control. arXiv:2309.15462, 2023.","identifier":"arXiv:2309.15462","url":"https://arxiv.org/abs/2309.15462"},{"text":"I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath. Real-World Humanoid Locomotion with Reinforcement Learning. arXiv:2303.03381, 2023.","identifier":"arXiv:2303.03381","url":"https://arxiv.org/abs/2303.03381"},{"text":"X. Gu, Y.-J. Wang, and J. Chen. Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer. arXiv:2404.05695, 2024.","identifier":"arXiv:2404.05695","url":"https://arxiv.org/abs/2404.05695"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"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":"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"},{"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":"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":"E. Perez, F. Strub, H. de Vries, V. Dumoulin, and A. Courville. FiLM: Visual Reasoning with a General Conditioning Layer. arXiv:1709.07871, 2017.","identifier":"arXiv:1709.07871","url":"https://arxiv.org/abs/1709.07871"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2607.00005","metadata":{"title":"Cross-Embodiment Distillation of Local Navigation Planners from Humanoid to Quadruped Teams","authors":[{"name":"Yifan Chen","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Zhiyuan Wang","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Haoran Liu","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Xiaotong Zhang","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"}],"publication_date":"2026-07-21","abstract":"Configuration-driven local planners such as AgniNav can serve several robot embodiments from a single planning stack, but the full planner is too expensive for the onboard computers of commodity quadrupeds. This preprint presents a cross-embodiment distillation pipeline in which a configuration-driven teacher planner generates local navigation trajectories in a shared, body-normalized configuration space, and a compact onboard student policy is distilled from these trajectories with an explicit embodiment descriptor as input. Training combines on-policy data aggregation, a finite-difference consistency regularizer that aligns the sensitivity of the student to descriptor changes with that of the teacher, and a held-out embodiment split used for model selection. The pipeline is evaluated in Isaac Lab with a Unitree Go2 and a 12-DoF H1-class humanoid over 20 indoor-outdoor transition scenes, 5 seeds, and 50 episodes per scene and seed, and onboard on a Go2 whose Jetson Orin computer runs the student. The distilled student reaches 90.4 percent navigation success, compared with 81.5 percent for a per-embodiment oracle teacher and 82.3 percent for a student without embodiment conditioning, with a teacher parity score of 93.1 percent and an onboard decision latency of 8.2 ms. Its mean path clearance margin of 18.7 cm is, however, the smallest among the compared methods. Ablations show drops of 11.3 to 15.3 percentage points when the descriptor input, the consistency regularizer, or the held-out split is removed. Recorded failures include reduced clearance on a steep entry ramp and degraded success under descriptor dropout.","references":[{"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":"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":"J. Zhang, Y. Du, X. Guo, S. Sun, X. Liu, Y. Sun, G. Lu, W. Sui, and J. Li. EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness. arXiv:2607.19880, 2026.","identifier":"arXiv:2607.19880","url":"https://arxiv.org/abs/2607.19880"},{"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":"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":"A. Sridhar, D. Shah, C. Glossop, and S. Levine. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. arXiv:2310.07896, 2023.","identifier":"arXiv:2310.07896","url":"https://arxiv.org/abs/2310.07896"},{"text":"A. Eftekhar et al.. The One RING: a Robotic Indoor Navigation Generalist. arXiv:2412.14401, 2024.","identifier":"arXiv:2412.14401","url":"https://arxiv.org/abs/2412.14401"},{"text":"J. Zhang et al.. Embodied Navigation Foundation Model. arXiv:2509.12129, 2025.","identifier":"arXiv:2509.12129","url":"https://arxiv.org/abs/2509.12129"},{"text":"J. Yang, C. Glossop, A. Bhorkar, D. Shah, Q. Vuong, C. Finn, D. Sadigh, and S. Levine. Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation. arXiv:2402.19432, 2024.","identifier":"arXiv:2402.19432","url":"https://arxiv.org/abs/2402.19432"},{"text":"R. Doshi, H. Walke, O. Mees, S. Dasari, and S. Levine. Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation. arXiv:2408.11812, 2024.","identifier":"arXiv:2408.11812","url":"https://arxiv.org/abs/2408.11812"},{"text":"Open X-Embodiment Collaboration. Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv:2310.08864, 2023.","identifier":"arXiv:2310.08864","url":"https://arxiv.org/abs/2310.08864"},{"text":"T. Chen, A. Murali, and A. Gupta. Hardware Conditioned Policies for Multi-Robot Transfer Learning. arXiv:1811.09864, 2018.","identifier":"arXiv:1811.09864","url":"https://arxiv.org/abs/1811.09864"},{"text":"W. Huang, I. Mordatch, and D. Pathak. One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control. arXiv:2007.04976, 2020.","identifier":"arXiv:2007.04976","url":"https://arxiv.org/abs/2007.04976"},{"text":"A. Gupta, L. Fan, S. Ganguli, and L. Fei-Fei. MetaMorph: Learning Universal Controllers with Transformers. arXiv:2203.11931, 2022.","identifier":"arXiv:2203.11931","url":"https://arxiv.org/abs/2203.11931"},{"text":"G. Feng et al.. GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots. arXiv:2209.05309, 2022.","identifier":"arXiv:2209.05309","url":"https://arxiv.org/abs/2209.05309"},{"text":"M. Shafiee, G. Bellegarda, and A. Ijspeert. ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots. arXiv:2310.10486, 2023.","identifier":"arXiv:2310.10486","url":"https://arxiv.org/abs/2310.10486"},{"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. Hinton, O. Vinyals, and J. Dean. Distilling the Knowledge in a Neural Network. arXiv:1503.02531, 2015.","identifier":"arXiv:1503.02531","url":"https://arxiv.org/abs/1503.02531"},{"text":"A. A. Rusu et al.. Policy Distillation. arXiv:1511.06295, 2015.","identifier":"arXiv:1511.06295","url":"https://arxiv.org/abs/1511.06295"},{"text":"S. Ross, G. J. Gordon, and J. A. Bagnell. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. arXiv:1011.0686, 2010.","identifier":"arXiv:1011.0686","url":"https://arxiv.org/abs/1011.0686"},{"text":"Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. Theodorou, and B. Boots. Agile Autonomous Driving using End-to-End Deep Imitation Learning. arXiv:1709.07174, 2017.","identifier":"arXiv:1709.07174","url":"https://arxiv.org/abs/1709.07174"},{"text":"D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl. Learning by Cheating. arXiv:1912.12294, 2019.","identifier":"arXiv:1912.12294","url":"https://arxiv.org/abs/1912.12294"},{"text":"E. Kaufmann, A. Loquercio, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza. Deep Drone Acrobatics. arXiv:2006.05768, 2020.","identifier":"arXiv:2006.05768","url":"https://arxiv.org/abs/2006.05768"},{"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":"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":"W. M. Czarnecki, S. Osindero, M. Jaderberg, G. Swirszcz, and R. Pascanu. Sobolev Training for Neural Networks. arXiv:1706.04859, 2017.","identifier":"arXiv:1706.04859","url":"https://arxiv.org/abs/1706.04859"},{"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":"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":"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. 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 et al.. Robot Parkour Learning. arXiv:2309.05665, 2023.","identifier":"arXiv:2309.05665","url":"https://arxiv.org/abs/2309.05665"},{"text":"F. Jenelten, J. He, F. Farshidian, and M. Hutter. DTC: Deep Tracking Control. arXiv:2309.15462, 2023.","identifier":"arXiv:2309.15462","url":"https://arxiv.org/abs/2309.15462"},{"text":"I. Radosavovic, T. Xiao, B. Zhang, T. Darrell, J. Malik, and K. Sreenath. Real-World Humanoid Locomotion with Reinforcement Learning. arXiv:2303.03381, 2023.","identifier":"arXiv:2303.03381","url":"https://arxiv.org/abs/2303.03381"},{"text":"X. Gu, Y.-J. Wang, and J. Chen. Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer. arXiv:2404.05695, 2024.","identifier":"arXiv:2404.05695","url":"https://arxiv.org/abs/2404.05695"},{"text":"V. Makoviychuk et al.. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. arXiv:2108.10470, 2021.","identifier":"arXiv:2108.10470","url":"https://arxiv.org/abs/2108.10470"},{"text":"M. Mittal et al.. Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments. arXiv:2301.04195, 2023.","identifier":"arXiv:2301.04195","url":"https://arxiv.org/abs/2301.04195"},{"text":"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":"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"},{"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":"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":"E. Perez, F. Strub, H. de Vries, V. Dumoulin, and A. Courville. 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