{"kagg_id":"KAGG:2609.00008","slug":"2609.00008","title":"Local Planning under Asymmetric Collision Geometries Across Embodiments","authors":[{"name":"Anika Roth","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Selin Aydin","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Lukas Brandt","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"}],"affiliation":null,"abstract":"Local planners that run across quadrupeds, wheeled robots, and humanoids commonly represent every body by a symmetrically inflated disk or a static footprint, discarding the swing envelope of the legs, humanoid headroom, and the directional structure of clearance. This paper builds an embodiment-accurate collision layer on the AgniNav configuration-driven backbone. The layer replaces the static envelope with a configuration-dependent collision set, formed by the footprint and a phase-indexed leg-swing volume for quadrupeds, a body envelope for wheeled rovers, and a full-body capsule with arm reach and a headroom term for humanoids, and it scores candidate trajectories with a clearance cost that is anisotropic in each body frame. The planner is evaluated in 15 Isaac Lab mixed-embodiment scenes with diagonal gaps of 45 to 75 degrees, cluttered corridors, and multi-robot proximity, across four embodiments, five seeds, and 30 episodes each. Relative to symmetric inflation, the method raises the feasible passage width gain from 14.2 to 14.8 percent, lowers the collision rate from 0.19 to 0.18 per km, and lowers the 95th-percentile planner runtime from 6.9 to 6.4 ms, while the aggregate spurious-stoppage rate is unchanged at 0.3 per episode. The aggregate gains are modest and concentrate in specific situations: in a 60 degree, 71 cm diagonal gap where the symmetric-inflation quadruped fails 12 of 30 attempts, the swing-aware quadruped passes at 1.2 m/s with 8 cm clearance, and in one corridor the full-body humanoid model reduces spurious stoppages near a wheeled robot from 3.1 to 0.4 per episode.","publication_date":"2026-09-22","registered_at":"2026-09-25T10:45:00Z","updated_at":"2026-09-27T16:26:00Z","status":"published","html_path":null,"pdf_path":"articles/2609.00008/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":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"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":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23-33, doi:10.1109/100.580977, 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"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":"X. Zhang, A. Liniger, and F. Borrelli. Optimization-Based Collision Avoidance. arXiv:1711.03449, 2017.","identifier":"arXiv:1711.03449","url":"https://arxiv.org/abs/1711.03449"},{"text":"H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning. arXiv:1611.03631, 2016.","identifier":"arXiv:1611.03631","url":"https://arxiv.org/abs/1611.03631"},{"text":"M. Gaertner, M. Bjelonic, F. Farshidian, and M. Hutter. Collision-Free MPC for Legged Robots in Static and Dynamic Scenes. arXiv:2103.13987, 2021.","identifier":"arXiv:2103.13987","url":"https://arxiv.org/abs/2103.13987"},{"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":"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":"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":"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":"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":"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":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. arXiv:2401.17583, 2024.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"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":"H.-T. L. Chiang, A. Faust, M. Fiser, and A. Francis. Learning Navigation Behaviors End-to-End with AutoRL. arXiv:1809.10124, 2018.","identifier":"arXiv:1809.10124","url":"https://arxiv.org/abs/1809.10124"},{"text":"Y. F. Chen, M. Liu, M. Everett, and J. P. How. Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning. arXiv:1609.07845, 2016.","identifier":"arXiv:1609.07845","url":"https://arxiv.org/abs/1609.07845"},{"text":"M. Everett, Y. F. Chen, and J. P. How. Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. arXiv:1805.01956, 2018.","identifier":"arXiv:1805.01956","url":"https://arxiv.org/abs/1805.01956"},{"text":"P. Long, T. Fan, X. Liao, W. Liu, H. Zhang, and J. Pan. Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. arXiv:1709.10082, 2017.","identifier":"arXiv:1709.10082","url":"https://arxiv.org/abs/1709.10082"},{"text":"C. Chen, Y. Liu, S. Kreiss, and A. Alahi. Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning. arXiv:1809.08835, 2018.","identifier":"arXiv:1809.08835","url":"https://arxiv.org/abs/1809.08835"},{"text":"C. Mavrogiannis et al.. Core Challenges of Social Robot Navigation: A Survey. arXiv:2103.05668, 2021.","identifier":"arXiv:2103.05668","url":"https://arxiv.org/abs/2103.05668"},{"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":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. arXiv:1709.07523, 2017.","identifier":"arXiv:1709.07523","url":"https://arxiv.org/abs/1709.07523"},{"text":"J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin. A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems. arXiv:1705.01292, 2017.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"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":"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":"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. 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":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"}],"canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2609.00008","metadata":{"title":"Local Planning under Asymmetric Collision Geometries Across Embodiments","authors":[{"name":"Anika Roth","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Selin Aydin","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"},{"name":"Lukas Brandt","affiliation":"Robot Learning Lab, University of Zurich, Zurich, Switzerland"}],"publication_date":"2026-09-22","abstract":"Local planners that run across quadrupeds, wheeled robots, and humanoids commonly represent every body by a symmetrically inflated disk or a static footprint, discarding the swing envelope of the legs, humanoid headroom, and the directional structure of clearance. This paper builds an embodiment-accurate collision layer on the AgniNav configuration-driven backbone. The layer replaces the static envelope with a configuration-dependent collision set, formed by the footprint and a phase-indexed leg-swing volume for quadrupeds, a body envelope for wheeled rovers, and a full-body capsule with arm reach and a headroom term for humanoids, and it scores candidate trajectories with a clearance cost that is anisotropic in each body frame. The planner is evaluated in 15 Isaac Lab mixed-embodiment scenes with diagonal gaps of 45 to 75 degrees, cluttered corridors, and multi-robot proximity, across four embodiments, five seeds, and 30 episodes each. Relative to symmetric inflation, the method raises the feasible passage width gain from 14.2 to 14.8 percent, lowers the collision rate from 0.19 to 0.18 per km, and lowers the 95th-percentile planner runtime from 6.9 to 6.4 ms, while the aggregate spurious-stoppage rate is unchanged at 0.3 per episode. The aggregate gains are modest and concentrate in specific situations: in a 60 degree, 71 cm diagonal gap where the symmetric-inflation quadruped fails 12 of 30 attempts, the swing-aware quadruped passes at 1.2 m/s with 8 cm clearance, and in one corridor the full-body humanoid model reduces spurious stoppages near a wheeled robot from 3.1 to 0.4 per episode.","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":"J. Hu, S. Yuan, G. C. R. Bethala, A. Tzes, and Y. Fang. Learning Adaptive Safety Margins for Visual Navigation. arXiv:2607.18200, 2026.","identifier":"arXiv:2607.18200","url":"https://arxiv.org/abs/2607.18200"},{"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":"D. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23-33, doi:10.1109/100.580977, 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"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":"X. Zhang, A. Liniger, and F. Borrelli. Optimization-Based Collision Avoidance. arXiv:1711.03449, 2017.","identifier":"arXiv:1711.03449","url":"https://arxiv.org/abs/1711.03449"},{"text":"H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto. Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning. arXiv:1611.03631, 2016.","identifier":"arXiv:1611.03631","url":"https://arxiv.org/abs/1611.03631"},{"text":"M. Gaertner, M. Bjelonic, F. Farshidian, and M. Hutter. Collision-Free MPC for Legged Robots in Static and Dynamic Scenes. arXiv:2103.13987, 2021.","identifier":"arXiv:2103.13987","url":"https://arxiv.org/abs/2103.13987"},{"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":"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":"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":"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":"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":"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":"T. He, C. Zhang, W. Xiao, G. He, C. Liu, and G. Shi. Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion. arXiv:2401.17583, 2024.","identifier":"arXiv:2401.17583","url":"https://arxiv.org/abs/2401.17583"},{"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":"H.-T. L. Chiang, A. Faust, M. Fiser, and A. Francis. Learning Navigation Behaviors End-to-End with AutoRL. arXiv:1809.10124, 2018.","identifier":"arXiv:1809.10124","url":"https://arxiv.org/abs/1809.10124"},{"text":"Y. F. Chen, M. Liu, M. Everett, and J. P. How. Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning. arXiv:1609.07845, 2016.","identifier":"arXiv:1609.07845","url":"https://arxiv.org/abs/1609.07845"},{"text":"M. Everett, Y. F. Chen, and J. P. How. Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. arXiv:1805.01956, 2018.","identifier":"arXiv:1805.01956","url":"https://arxiv.org/abs/1805.01956"},{"text":"P. Long, T. Fan, X. Liao, W. Liu, H. Zhang, and J. Pan. Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning. arXiv:1709.10082, 2017.","identifier":"arXiv:1709.10082","url":"https://arxiv.org/abs/1709.10082"},{"text":"C. Chen, Y. Liu, S. Kreiss, and A. Alahi. Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning. arXiv:1809.08835, 2018.","identifier":"arXiv:1809.08835","url":"https://arxiv.org/abs/1809.08835"},{"text":"C. Mavrogiannis et al.. Core Challenges of Social Robot Navigation: A Survey. arXiv:2103.05668, 2021.","identifier":"arXiv:2103.05668","url":"https://arxiv.org/abs/2103.05668"},{"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":"S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin. Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances. arXiv:1709.07523, 2017.","identifier":"arXiv:1709.07523","url":"https://arxiv.org/abs/1709.07523"},{"text":"J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin. A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems. arXiv:1705.01292, 2017.","identifier":"arXiv:1705.01292","url":"https://arxiv.org/abs/1705.01292"},{"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":"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":"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. 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":"P. Anderson et al.. On Evaluation of Embodied Navigation Agents. arXiv:1807.06757, 2018.","identifier":"arXiv:1807.06757","url":"https://arxiv.org/abs/1807.06757"}],"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:2609.00008","slug":"2609.00008","registered_at":"2026-09-25T10:45:00Z","updated_at":"2026-09-27T16:26:00Z","canonical_url":"https://academy.kaggleyes.top/abs/KAGG:2609.00008","resolver_url":"https://academy.kaggleyes.top/id/KAGG:2609.00008","tex_path":"articles/2609.00008/source.tex","typesetting":{"class":"kaggpreprint","version":"v1.0","engine":"XeLaTeX","rendered_at":"2026-09-25T13:02:00Z"}},"resolver_url":"https://academy.kaggleyes.top/id/KAGG:2609.00008"}