{"kagg_id":"KAGG:2609.00005","slug":"2609.00005","title":"Shared Configuration Abstractions for Heterogeneous Fleet Navigation","authors":[{"name":"Hana Sato","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Tomas Keller","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Mei-Lin Chao","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"}],"affiliation":null,"abstract":"Heterogeneous robot fleets that must follow, avoid, or rendezvous with one another need more than per-robot planning: each robot must reason about how its peers are shaped, how quickly they can stop, and how these properties change during operation. Configuration-driven local planners such as AgniNav describe a single robot through an abstract configuration of body geometry, sensing, and velocity limits, but they treat other robots as generic obstacles. This paper extends the configuration descriptor into a fleet-shared abstraction. Each robot publishes a versioned configuration handle on the fleet bus, and the unchanged local planner evaluates interaction-critical derived quantities, namely a predicted clearance envelope and a stop distance, as functions of the peer's live configuration, with conservative inflation when a handle is stale. The approach is evaluated in simulation on a mixed fleet of a Go2-class quadruped, a B2-class quadruped, and a wheeled robot across 12 corridor and intersection scenarios with 5 seeds each. Relative to a configuration-blind planner that models peers as footprint blobs, interaction success increases from 77.7 to 91.6 percent, envelope prediction error decreases from 12.2 cm to 10.8 cm, and stop-distance error decreases from 15.3 cm to 14.3 cm, while the 95th-percentile planner overhead is 3.1 ms compared with 3.5 ms. Ablations indicate that the stop-distance derivation, the clearance envelope, and live configuration updates each account for 9.0 to 10.1 percentage points of interaction success. Recorded failure cases illustrate behavior under stale handles and mid-episode payload changes.","publication_date":"2026-09-10","registered_at":"2026-09-17T18:00:00Z","updated_at":"2026-09-17T19:09:00Z","status":"published","html_path":null,"pdf_path":"articles/2609.00005/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":"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. 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. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1), 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"C. Rösmann, F. Hoffmann, and T. Bertram. Integrated Online Trajectory Planning and Optimization in Distinctive Topologies. Robotics and Autonomous Systems, 88, 2017.","identifier":"doi:10.1016/j.robot.2016.11.007","url":"https://doi.org/10.1016/j.robot.2016.11.007"},{"text":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"doi:10.1109/ICRA.2017.7989202","url":"https://doi.org/10.1109/ICRA.2017.7989202"},{"text":"S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020.","identifier":"arXiv:2003.00368","url":"https://arxiv.org/abs/2003.00368"},{"text":"S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall. Robot Operating System 2: Design, Architecture, and Uses in the Wild. arXiv:2211.07752, 2022.","identifier":"arXiv:2211.07752","url":"https://arxiv.org/abs/2211.07752"},{"text":"P. Fiorini and Z. Shiller. Motion Planning in Dynamic Environments Using Velocity Obstacles. The International Journal of Robotics Research, 17(7), 1998.","identifier":"doi:10.1177/027836499801700706","url":"https://doi.org/10.1177/027836499801700706"},{"text":"J. van den Berg, M. Lin, and D. Manocha. Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation. IEEE International Conference on Robotics and Automation (ICRA), 2008.","identifier":"doi:10.1109/ROBOT.2008.4543489","url":"https://doi.org/10.1109/ROBOT.2008.4543489"},{"text":"J. van den Berg, S. J. Guy, M. Lin, and D. Manocha. Reciprocal n-Body Collision Avoidance. Robotics Research (ISRR), Springer Tracts in Advanced Robotics, vol. 70, 2011.","identifier":"doi:10.1007/978-3-642-19457-3_1","url":"https://doi.org/10.1007/978-3-642-19457-3_1"},{"text":"J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart. Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots. Distributed Autonomous Robotic Systems, Springer Tracts in Advanced Robotics, vol. 83, 2013.","identifier":"doi:10.1007/978-3-642-32723-0_15","url":"https://doi.org/10.1007/978-3-642-32723-0_15"},{"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":"L. Wang, A. D. Ames, and M. Egerstedt. Safety Barrier Certificates for Collisions-Free Multirobot Systems. IEEE Transactions on Robotics, 33(3), 2017.","identifier":"doi:10.1109/TRO.2017.2659727","url":"https://doi.org/10.1109/TRO.2017.2659727"},{"text":"S. Shalev-Shwartz, S. Shammah, and A. Shashua. On a Formal Model of Safe and Scalable Self-driving Cars. arXiv:1708.06374, 2017.","identifier":"arXiv:1708.06374","url":"https://arxiv.org/abs/1708.06374"},{"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":"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":"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":"X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao. EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments. arXiv:2011.04183, 2020.","identifier":"arXiv:2011.04183","url":"https://arxiv.org/abs/2011.04183"},{"text":"J. Tordesillas and J. P. How. MADER: Trajectory Planner in Multi-Agent and Dynamic Environments. arXiv:2010.04466, 2020.","identifier":"arXiv:2010.04466","url":"https://arxiv.org/abs/2010.04466"},{"text":"C. E. Luis and A. P. Schoellig. Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control. arXiv:1809.04230, 2018.","identifier":"arXiv:1809.04230","url":"https://arxiv.org/abs/1809.04230"},{"text":"G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant. Conflict-Based Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, 219, 2015.","identifier":"doi:10.1016/j.artint.2014.11.006","url":"https://doi.org/10.1016/j.artint.2014.11.006"},{"text":"L. E. Parker. ALLIANCE: An Architecture for Fault Tolerant Multirobot Cooperation. IEEE Transactions on Robotics and Automation, 14(2), 1998.","identifier":"doi:10.1109/70.681242","url":"https://doi.org/10.1109/70.681242"},{"text":"B. P. Gerkey and M. J. Matarić. A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems. The International Journal of Robotics Research, 23(9), 2004.","identifier":"doi:10.1177/0278364904045564","url":"https://doi.org/10.1177/0278364904045564"},{"text":"Y. Rizk, M. Awad, and E. W. Tunstel. Cooperative Heterogeneous Multi-Robot Systems: A Survey. ACM Computing Surveys, 52(2), 2019.","identifier":"doi:10.1145/3303848","url":"https://doi.org/10.1145/3303848"},{"text":"P. Trautman and A. Krause. Unfreezing the Robot: Navigation in Dense, Interacting Crowds. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010.","identifier":"doi:10.1109/IROS.2010.5654369","url":"https://doi.org/10.1109/IROS.2010.5654369"},{"text":"C. Mavrogiannis, F. Baldini, A. Wang, D. Zhao, P. Trautman, A. Steinfeld, and J. Oh. 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. Francis et al.. Principles and Guidelines for Evaluating Social Robot Navigation Algorithms. arXiv:2306.16740, 2023.","identifier":"arXiv:2306.16740","url":"https://arxiv.org/abs/2306.16740"},{"text":"M. Hutter et al.. ANYmal - a Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7758092","url":"https://doi.org/10.1109/IROS.2016.7758092"},{"text":"J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.","identifier":"doi:10.1109/IROS.2018.8594448","url":"https://doi.org/10.1109/IROS.2018.8594448"},{"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":"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":"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":"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. 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:2609.00005","metadata":{"title":"Shared Configuration Abstractions for Heterogeneous Fleet Navigation","authors":[{"name":"Hana Sato","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Tomas Keller","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"},{"name":"Mei-Lin Chao","affiliation":"Advanced Robotics Group, Toyota Research Institute, Palo Alto, CA, USA"}],"publication_date":"2026-09-10","abstract":"Heterogeneous robot fleets that must follow, avoid, or rendezvous with one another need more than per-robot planning: each robot must reason about how its peers are shaped, how quickly they can stop, and how these properties change during operation. Configuration-driven local planners such as AgniNav describe a single robot through an abstract configuration of body geometry, sensing, and velocity limits, but they treat other robots as generic obstacles. This paper extends the configuration descriptor into a fleet-shared abstraction. Each robot publishes a versioned configuration handle on the fleet bus, and the unchanged local planner evaluates interaction-critical derived quantities, namely a predicted clearance envelope and a stop distance, as functions of the peer's live configuration, with conservative inflation when a handle is stale. The approach is evaluated in simulation on a mixed fleet of a Go2-class quadruped, a B2-class quadruped, and a wheeled robot across 12 corridor and intersection scenarios with 5 seeds each. Relative to a configuration-blind planner that models peers as footprint blobs, interaction success increases from 77.7 to 91.6 percent, envelope prediction error decreases from 12.2 cm to 10.8 cm, and stop-distance error decreases from 15.3 cm to 14.3 cm, while the 95th-percentile planner overhead is 3.1 ms compared with 3.5 ms. Ablations indicate that the stop-distance derivation, the clearance envelope, and live configuration updates each account for 9.0 to 10.1 percentage points of interaction success. Recorded failure cases illustrate behavior under stale handles and mid-episode payload changes.","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":"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. 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. Fox, W. Burgard, and S. Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1), 1997.","identifier":"doi:10.1109/100.580977","url":"https://doi.org/10.1109/100.580977"},{"text":"C. Rösmann, F. Hoffmann, and T. Bertram. Integrated Online Trajectory Planning and Optimization in Distinctive Topologies. Robotics and Autonomous Systems, 88, 2017.","identifier":"doi:10.1016/j.robot.2016.11.007","url":"https://doi.org/10.1016/j.robot.2016.11.007"},{"text":"G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou. Information Theoretic MPC for Model-Based Reinforcement Learning. IEEE International Conference on Robotics and Automation (ICRA), 2017.","identifier":"doi:10.1109/ICRA.2017.7989202","url":"https://doi.org/10.1109/ICRA.2017.7989202"},{"text":"S. Macenski, F. Martín, R. White, and J. Ginés Clavero. The Marathon 2: A Navigation System. arXiv:2003.00368, 2020.","identifier":"arXiv:2003.00368","url":"https://arxiv.org/abs/2003.00368"},{"text":"S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall. Robot Operating System 2: Design, Architecture, and Uses in the Wild. arXiv:2211.07752, 2022.","identifier":"arXiv:2211.07752","url":"https://arxiv.org/abs/2211.07752"},{"text":"P. Fiorini and Z. Shiller. Motion Planning in Dynamic Environments Using Velocity Obstacles. The International Journal of Robotics Research, 17(7), 1998.","identifier":"doi:10.1177/027836499801700706","url":"https://doi.org/10.1177/027836499801700706"},{"text":"J. van den Berg, M. Lin, and D. Manocha. Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation. IEEE International Conference on Robotics and Automation (ICRA), 2008.","identifier":"doi:10.1109/ROBOT.2008.4543489","url":"https://doi.org/10.1109/ROBOT.2008.4543489"},{"text":"J. van den Berg, S. J. Guy, M. Lin, and D. Manocha. Reciprocal n-Body Collision Avoidance. Robotics Research (ISRR), Springer Tracts in Advanced Robotics, vol. 70, 2011.","identifier":"doi:10.1007/978-3-642-19457-3_1","url":"https://doi.org/10.1007/978-3-642-19457-3_1"},{"text":"J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart. Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots. Distributed Autonomous Robotic Systems, Springer Tracts in Advanced Robotics, vol. 83, 2013.","identifier":"doi:10.1007/978-3-642-32723-0_15","url":"https://doi.org/10.1007/978-3-642-32723-0_15"},{"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":"L. Wang, A. D. Ames, and M. Egerstedt. Safety Barrier Certificates for Collisions-Free Multirobot Systems. IEEE Transactions on Robotics, 33(3), 2017.","identifier":"doi:10.1109/TRO.2017.2659727","url":"https://doi.org/10.1109/TRO.2017.2659727"},{"text":"S. Shalev-Shwartz, S. Shammah, and A. Shashua. On a Formal Model of Safe and Scalable Self-driving Cars. arXiv:1708.06374, 2017.","identifier":"arXiv:1708.06374","url":"https://arxiv.org/abs/1708.06374"},{"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":"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":"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":"X. Zhou, J. Zhu, H. Zhou, C. Xu, and F. Gao. EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments. arXiv:2011.04183, 2020.","identifier":"arXiv:2011.04183","url":"https://arxiv.org/abs/2011.04183"},{"text":"J. Tordesillas and J. P. How. MADER: Trajectory Planner in Multi-Agent and Dynamic Environments. arXiv:2010.04466, 2020.","identifier":"arXiv:2010.04466","url":"https://arxiv.org/abs/2010.04466"},{"text":"C. E. Luis and A. P. Schoellig. Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control. arXiv:1809.04230, 2018.","identifier":"arXiv:1809.04230","url":"https://arxiv.org/abs/1809.04230"},{"text":"G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant. Conflict-Based Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, 219, 2015.","identifier":"doi:10.1016/j.artint.2014.11.006","url":"https://doi.org/10.1016/j.artint.2014.11.006"},{"text":"L. E. Parker. ALLIANCE: An Architecture for Fault Tolerant Multirobot Cooperation. IEEE Transactions on Robotics and Automation, 14(2), 1998.","identifier":"doi:10.1109/70.681242","url":"https://doi.org/10.1109/70.681242"},{"text":"B. P. Gerkey and M. J. Matarić. A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems. The International Journal of Robotics Research, 23(9), 2004.","identifier":"doi:10.1177/0278364904045564","url":"https://doi.org/10.1177/0278364904045564"},{"text":"Y. Rizk, M. Awad, and E. W. Tunstel. Cooperative Heterogeneous Multi-Robot Systems: A Survey. ACM Computing Surveys, 52(2), 2019.","identifier":"doi:10.1145/3303848","url":"https://doi.org/10.1145/3303848"},{"text":"P. Trautman and A. Krause. Unfreezing the Robot: Navigation in Dense, Interacting Crowds. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010.","identifier":"doi:10.1109/IROS.2010.5654369","url":"https://doi.org/10.1109/IROS.2010.5654369"},{"text":"C. Mavrogiannis, F. Baldini, A. Wang, D. Zhao, P. Trautman, A. Steinfeld, and J. Oh. 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. Francis et al.. Principles and Guidelines for Evaluating Social Robot Navigation Algorithms. arXiv:2306.16740, 2023.","identifier":"arXiv:2306.16740","url":"https://arxiv.org/abs/2306.16740"},{"text":"M. Hutter et al.. ANYmal - a Highly Mobile and Dynamic Quadrupedal Robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.","identifier":"doi:10.1109/IROS.2016.7758092","url":"https://doi.org/10.1109/IROS.2016.7758092"},{"text":"J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim. Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.","identifier":"doi:10.1109/IROS.2018.8594448","url":"https://doi.org/10.1109/IROS.2018.8594448"},{"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":"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":"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":"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. Kadian et al.. 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