{"kagg_id":"KAGG:2609.00002","slug":"2609.00002","title":"Distilling Cross-Embodiment Planners into Onboard-Deployable Policies","authors":[{"name":"Rongxin Guo","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"},{"name":"Yifan Chen","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Daichi Mori","affiliation":"Legged Systems Group, The University of Tokyo, Tokyo, Japan"}],"affiliation":null,"abstract":"Configuration-driven cross-embodiment local planners such as AgniNav allow a single planning stack to serve robots with different body geometry, sensing, and velocity limits, but they pay for this generality with full configuration-space sampling at every replan. On an embedded computer this cost is prohibitive: the full planner requires 41 ms per decision offline, and throttling it onboard degrades agreement with its unthrottled output. This paper distills such a planner into a configuration-conditioned onboard policy through trajectory-level imitation. The teacher produces local trajectories over configuration states; the student maps an observation and a configuration descriptor directly to the next body-motion primitive from a velocity-normalized library. Training combines a soft-target imitation term with a consistency regularizer that penalizes, under perturbed range observations, probability mass on primitives that diverge geometrically from the teacher decision. Because the student remains conditioned on the descriptor, one deployed network serves multiple embodiments. In Isaac Lab experiments with a Unitree Go2 across 18 scenes and 5 seeds, with the student running at 100 Hz on an onboard Orin, the distilled policy reaches a p95 decision latency of 7.8 ms, a teacher parity of 94.3 percent, and a success rate of 91.7 percent, compared with 8.7 ms, 84.2 percent, and 86.3 percent for the throttled full planner. Transfer to a B2-class morphology in simulation retains 89.6 percent parity, and at 10 percent range dropout the regularizer preserves 88.1 percent parity versus 79.4 percent without it.","publication_date":"2026-09-03","registered_at":"2026-09-09T18:00:00Z","updated_at":"2026-09-10T18:17:00Z","status":"published","html_path":null,"pdf_path":"articles/2609.00002/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":"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":"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":"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":"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":"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":"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, H. Zhang, Z. Li, X. B. Peng, B. Basireddy, L. Yue, Z. Song, L. Yang, Y. Liu, K. Sreenath, and S. Levine. 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":"N. Bohlinger, G. Czechmanowski, M. Krupka, P. Kicki, K. Walas, J. Peters, and D. Tateo. One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion. arXiv:2409.06366, 2024.","identifier":"arXiv:2409.06366","url":"https://arxiv.org/abs/2409.06366"},{"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, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell. Policy Distillation. arXiv:1511.06295, 2015.","identifier":"arXiv:1511.06295","url":"https://arxiv.org/abs/1511.06295"},{"text":"W. M. Czarnecki, R. Pascanu, S. Osindero, S. M. Jayakumar, G. Swirszcz, and M. Jaderberg. Distilling Policy Distillation. arXiv:1902.02186, 2019.","identifier":"arXiv:1902.02186","url":"https://arxiv.org/abs/1902.02186"},{"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, 2011.","identifier":"arXiv:1011.0686","url":"https://arxiv.org/abs/1011.0686"},{"text":"S. Levine, C. Finn, T. Darrell, and P. Abbeel. End-to-End Training of Deep Visuomotor Policies. arXiv:1504.00702, 2015.","identifier":"arXiv:1504.00702","url":"https://arxiv.org/abs/1504.00702"},{"text":"T. Zhang, G. Kahn, S. Levine, and P. Abbeel. Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search. arXiv:1509.06791, 2015.","identifier":"arXiv:1509.06791","url":"https://arxiv.org/abs/1509.06791"},{"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":"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":"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":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"A. 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":"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":"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":"M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena. From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots. arXiv:1609.07910, 2016.","identifier":"arXiv:1609.07910","url":"https://arxiv.org/abs/1609.07910"},{"text":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. arXiv:2011.13112, 2020.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"text":"X. Xiao, B. Liu, G. Warnell, J. Fink, and P. Stone. APPLD: Adaptive Planner Parameter Learning from Demonstration. arXiv:2004.00116, 2020.","identifier":"arXiv:2004.00116","url":"https://arxiv.org/abs/2004.00116"},{"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":"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":"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":"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":"C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song. Diffusion Policy: Visuomotor Policy Learning via Action Diffusion. arXiv:2303.04137, 2023.","identifier":"arXiv:2303.04137","url":"https://arxiv.org/abs/2303.04137"},{"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"},{"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":"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"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2609.00002","metadata":{"title":"Distilling Cross-Embodiment Planners into Onboard-Deployable Policies","authors":[{"name":"Rongxin Guo","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"},{"name":"Yifan Chen","affiliation":"Robotics and Autonomous Systems Lab, Shanghai Jiao Tong University, Shanghai, China"},{"name":"Daichi Mori","affiliation":"Legged Systems Group, The University of Tokyo, Tokyo, Japan"}],"publication_date":"2026-09-03","abstract":"Configuration-driven cross-embodiment local planners such as AgniNav allow a single planning stack to serve robots with different body geometry, sensing, and velocity limits, but they pay for this generality with full configuration-space sampling at every replan. On an embedded computer this cost is prohibitive: the full planner requires 41 ms per decision offline, and throttling it onboard degrades agreement with its unthrottled output. This paper distills such a planner into a configuration-conditioned onboard policy through trajectory-level imitation. The teacher produces local trajectories over configuration states; the student maps an observation and a configuration descriptor directly to the next body-motion primitive from a velocity-normalized library. Training combines a soft-target imitation term with a consistency regularizer that penalizes, under perturbed range observations, probability mass on primitives that diverge geometrically from the teacher decision. Because the student remains conditioned on the descriptor, one deployed network serves multiple embodiments. In Isaac Lab experiments with a Unitree Go2 across 18 scenes and 5 seeds, with the student running at 100 Hz on an onboard Orin, the distilled policy reaches a p95 decision latency of 7.8 ms, a teacher parity of 94.3 percent, and a success rate of 91.7 percent, compared with 8.7 ms, 84.2 percent, and 86.3 percent for the throttled full planner. Transfer to a B2-class morphology in simulation retains 89.6 percent parity, and at 10 percent range dropout the regularizer preserves 88.1 percent parity versus 79.4 percent without it.","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":"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":"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":"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":"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":"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":"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, H. Zhang, Z. Li, X. B. Peng, B. Basireddy, L. Yue, Z. Song, L. Yang, Y. Liu, K. Sreenath, and S. Levine. 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":"N. Bohlinger, G. Czechmanowski, M. Krupka, P. Kicki, K. Walas, J. Peters, and D. Tateo. One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion. arXiv:2409.06366, 2024.","identifier":"arXiv:2409.06366","url":"https://arxiv.org/abs/2409.06366"},{"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, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell. Policy Distillation. arXiv:1511.06295, 2015.","identifier":"arXiv:1511.06295","url":"https://arxiv.org/abs/1511.06295"},{"text":"W. M. Czarnecki, R. Pascanu, S. Osindero, S. M. Jayakumar, G. Swirszcz, and M. Jaderberg. Distilling Policy Distillation. arXiv:1902.02186, 2019.","identifier":"arXiv:1902.02186","url":"https://arxiv.org/abs/1902.02186"},{"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, 2011.","identifier":"arXiv:1011.0686","url":"https://arxiv.org/abs/1011.0686"},{"text":"S. Levine, C. Finn, T. Darrell, and P. Abbeel. End-to-End Training of Deep Visuomotor Policies. arXiv:1504.00702, 2015.","identifier":"arXiv:1504.00702","url":"https://arxiv.org/abs/1504.00702"},{"text":"T. Zhang, G. Kahn, S. Levine, and P. Abbeel. Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search. arXiv:1509.06791, 2015.","identifier":"arXiv:1509.06791","url":"https://arxiv.org/abs/1509.06791"},{"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":"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":"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":"A. Kumar, Z. Fu, D. Pathak, and J. Malik. RMA: Rapid Motor Adaptation for Legged Robots. arXiv:2107.04034, 2021.","identifier":"arXiv:2107.04034","url":"https://arxiv.org/abs/2107.04034"},{"text":"A. 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":"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":"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":"M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena. From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots. arXiv:1609.07910, 2016.","identifier":"arXiv:1609.07910","url":"https://arxiv.org/abs/1609.07910"},{"text":"X. Xiao, B. Liu, G. Warnell, and P. Stone. Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey. arXiv:2011.13112, 2020.","identifier":"arXiv:2011.13112","url":"https://arxiv.org/abs/2011.13112"},{"text":"X. Xiao, B. Liu, G. Warnell, J. Fink, and P. Stone. APPLD: Adaptive Planner Parameter Learning from Demonstration. arXiv:2004.00116, 2020.","identifier":"arXiv:2004.00116","url":"https://arxiv.org/abs/2004.00116"},{"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":"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":"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":"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":"C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song. Diffusion Policy: Visuomotor Policy Learning via Action Diffusion. arXiv:2303.04137, 2023.","identifier":"arXiv:2303.04137","url":"https://arxiv.org/abs/2303.04137"},{"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"},{"text":"V. Makoviychuk et al. 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