Publications

Our research spans across robotic problems from estimation through planning to control with a focus on efficent distributed algorithms for multi-robot platforms.

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Under Review | Accepted

  1. R. Firoozi, A. Mir, G. S. Camps, and M. Schwager, “Occlusion-Aware MPC for Guaranteed Safe Robot Navigation with Unseen Dynamic Obstacles,” 2022. Under Review. [pdf] [bibtex]
  2. T. Halsted and M. Schwager, “The Riemannian Elevator for certifiable distance-based localization.” 2022. Under Review. [pdf] [bibtex]
  3. T. A. Howell, S. Le Cleac’h, J. Z. Kolter, M. Schwager, and Z. Manchester, “Dojo: A Differentiable Simulator for Robotics,” Mar. 2022. Under Review. [pdf] [bibtex]
  4. J. A. Vincent and M. Schwager, “Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systems.” Oct. 2022. Under Review. [pdf] [bibtex]
  5. O. Shorinwa and M. Schwager, “Distributed Resource Allocation for Multi-Agent Networks.” Under Review. [pdf] [bibtex]

2024

  1. O. Shorinwa, J. Tucker, A. Smith, A. Swann, T. Chen, R. Firoozi, M. Kennedy III, and M. Schwager, “Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting.” arXiv, Nov. 2024, doi: 10.48550/arXiv.2405.04378. [pdf] [bibtex]
  2. T. Chen, P. Culbertson, and M. Schwager, “CATNIPS: Collision Avoidance Through Neural Implicit Probabilistic Scenes,” IEEE Transactions on Robotics, vol. 40, pp. 2712–2728, Apr. 2024. [pdf] [bibtex]
  3. O. Shorinwa, T. Halsted, J. Yu, and M. Schwager, “Distributed Optimization Methods for Multi-Robot Systems: Part II — A Survey,” IEEE Robotics & Automation Magazine, vol. 31, no. 3, pp. 154–169, Feb. 2024. [pdf] [bibtex]
  4. O. Shorinwa, T. Halsted, J. Yu, and M. Schwager, “Distributed Optimization Methods for Multi-Robot Systems: Part I — A Tutorial,” IEEE Robotics & Automation Magazine, vol. 31, no. 3, pp. 121–138, Feb. 2024. [pdf] [bibtex]
  5. S. Le Cleac’h, T. A. Howell, M. Schwager, and Z. Manchester, “Fast Contact-Implicit Model-Predictive Control,” Transactions on Robotics, vol. 40, Jan. 2024. [pdf] [bibtex]
  6. J. A. Vincent, A. O. Feldman, and M. Schwager, “Guarantees on Robot System Performance Using Stochastic Simulation Rollouts,” IEEE Transactions on Robotics, vol. 40, pp. 3984–4002, Aug. 2024. [pdf] [bibtex]
  7. J. A. Vincent, H. Nishimura, M. Itkina, P. Shah, M. Schwager, and T. Kollar, “How Generalizable Is My Behavior Cloning Policy? A Statistical Evaluation Approach to Trustworthy Performance Evaluation,” IEEE Robotics and Automation Letters, vol. 9, no. 10, pp. 8619–8626, Aug. 2024. [pdf] [bibtex]
  8. K. Nagami and M. Schwager, “State Estimation and Belief Space Planning under Epistemic Uncertainty for Learning-Based Perception Systems,” IEEE Robotics and Automation Letters, Mar. 2024. [pdf] [bibtex]

2023

  1. P. Washington, D. Fridovich-Keil, and M. Schwager, “GrAVITree: Graph-based Approximate Value Function In a Tree,” in 2023 American Controls Conference, Jun. 2023. [pdf] [bibtex]
  2. J. Sun, Y. Jiang, J. Qiu, P. T. Nobel, M. Kochenderfer, and M. Schwager, “Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model,” in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), Dec. 2023. [pdf] [bibtex]
  3. R. N. Haksar and M. Schwager, “Constrained Control of Large Graph-based MDPs Under Measurement Uncertainty,” IEEE Transactions on Automatic Control, pp. 1–16, 2023. [pdf] [bibtex]
  4. N. Mehr, M. Wang, M. Bhatt, and M. Schwager, “Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions,” IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1801–1815, Jun. 2023. [pdf] [bibtex]
  5. O. Shorinwa and M. Schwager, “Distributed Target Tracking in Multi-Agent Networks via Sequential Quadratic Alternating Direction Method of Multipliers,” in 2023 American Control Conference (ACC), May 2023, pp. 341–348. [pdf] [bibtex]
  6. O. Shorinwa, R. N. Haksar, P. Washington, and M. Schwager, “Distributed Multirobot Task Assignment via Consensus ADMM,” IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1781–1800, Jun. 2023. [pdf] [bibtex]
  7. O. Shorinwa and M. Schwager, “Distributed Model Predictive Control via Separable Optimization in Multi-Agent Networks,” IEEE Transactions on Automatic Control, pp. 1–16, 2023. [pdf] [bibtex]
  8. S. Le Cleac’h, H.-X. Yu, M. Guo, T. Howell, R. Gao, J. Wu, Z. Manchester, and M. Schwager, “Differentiable physics simulation of dynamics-augmented neural objects,” IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2780–2787, 2023. [pdf] [bibtex]
  9. M. Chahine, R. Firoozi, W. Xiao, M. Schwager, and D. Rus, “Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning,” IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1223–1230, Mar. 2023. [pdf] [bibtex]
  10. J. Sun, Y. Xu, M. Ding, H. Yi, C. Wang, J. Wang, L. Zhang, and M. Schwager, “NeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields,” IEEE Robotics and Automation Letters (RA-L), vol. 8, no. 8, pp. 5244–5250, Jul. 2023. [pdf] [bibtex]
  11. J. Sun, S. Kousik, D. Fridovich-Keil, and M. Schwager, “Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure,” in IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 2023. [pdf] [bibtex]

2022

  1. P. Culbertson, S. Bandyopadhyay, A. Goel, P. McGarey, and M. Schwager, “Multi-Robot Assembly Scheduling for the Lunar Crater Radio Telescope on the Far-Side of the Moon,” in 2022 IEEE Aerospace Conference, Feb. 2022. [pdf] [bibtex]
  2. R. Chandra, M. Wang, M. Schwager, and D. Manocha, “Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers,” in 2022 International Conference on Robotics and Automation (ICRA), May 2022, pp. 2876–2883. [pdf] [bibtex]
  3. J. Sun, S. Kousik, D. Fridovich-Keil, and M. Schwager, “Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic Prediction for Smart Cities,” in 25th IEEE Intelligent Transportation Systems Conference (ITSC 2022), Apr. 2022. [pdf] [bibtex]
  4. M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager, “Vision-Only Robot Navigation in a Neural Radiance World,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, pp. 4606–4613, Apr. 2022. [project page] [pdf] [bibtex]
  5. J. Yu, J. A. Vincent, and M. Schwager, “DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning,” IEEE Robotics and Automation Letters, Jan. 2022. [pdf] [bibtex]

2021

  1. T. Halsted, O. Shorinwa, J. Yu, and M. Schwager, “A Survey of Distributed Optimization Methods for Multi-Robot Systems,” arXiv:2103.12840 [cs], Mar. 2021. [pdf] [bibtex]
  2. A. Cauligi, P. Culbertson, E. Schmerling, M. Schwager, B. Stellato, and M. Pavone, “CoCo: Online Mixed-Integer Control via Supervised Learning,” IEEE Robotics and Automation Letters (RA-L), Jul. 2021. [pdf] [bibtex]
  3. P. Culbertson, J.-J. E. Slotine, and M. Schwager, “Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1906–1920, 2021. [code] [pdf] [bibtex]
  4. C. Chen, P. Culbertson, M. Lepert, M. Schwager, and J. Bohg, “TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2021, pp. 8262–8268. [bibtex]
  5. O. Shorinwa and M. Schwager, “Distributed Contact-Implicit Trajectory Optimization for Collaborative Manipulation,” in 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Nov. 2021, pp. 56–65. [pdf] [bibtex]
  6. R. N. Haksar and M. Schwager, “Learning Large Graph-based MDPs with Historical Data,” IEEE Transactions on Control of Network Systems, 2021. [pdf] [bibtex]
  7. J. A. Vincent and M. Schwager, “Reachable Polyhedral Marching (RPM): A Safety Verification Algorithm for Robotic Systems with Deep Neural Network Components,” in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021. [pdf] [bibtex]
  8. P. Washington and M. Schwager, “Reduced State Value Iteration for Multi-Drone Persistent Surveillance with Charging Constraints,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021, pp. 6367–6374. [pdf] [bibtex]
  9. S. Le Cleac’h, M. Schwager, and Z. Manchester, “ALGAMES: A Fast Augmented Lagrangian Solver for Constrained Dynamic Games,” Autonomous Robots, 2021. [pdf] [bibtex]
  10. S. Le Cleac’h, M. Schwager, and Z. Manchester, “LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5485–5492, 2021. [pdf] [bibtex]
  11. K. Nagami and M. Schwager, “HJB-RL: Initializing Reinforcement Learning with Optimal Control Policies Applied to Autonomous Drone Racing,” in Robotics: Science and Systems XVII, Jul. 2021. [pdf] [bibtex]
  12. H. Nishimura and M. Schwager, “SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control,” The International Journal of Robotics Research, vol. 40, no. 10-11, pp. 1167–1195, Aug. 2021. [pdf] [bibtex]
  13. H. Nishimura, N. Mehr, A. Gaidon, and M. Schwager, “RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 763–770, Apr. 2021. [pdf] [bibtex]
  14. M. Wang, Z. Wang, J. Talbot, J. C. Gerdes, and M. Schwager, “Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios,” IEEE Transactions on Robotics, 2021. [pdf] [bibtex]

2020

  1. B. Ramtoula, A. Caccavale, G. Beltrame, and M. Schwager, “MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception,” in Springer Proceedings in Advanced Robotics, 2020, vol. 19, pp. 520–532. [pdf] [bibtex]
  2. A. Cauligi, P. Culbertson, B. Stellato, D. Bertsimas, M. Schwager, and M. Pavone, “Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control,” in 2020 59th IEEE Conference on Decision and Control (CDC), Dec. 2020, pp. 1698–1705. [code] [pdf] [bibtex]
  3. O. Shorinwa and M. Schwager, “Scalable Collaborative Manipulation with Distributed Trajectory Planning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 9108–9115. [pdf] [bibtex]
  4. O. Shorinwa, T. Halsted, and M. Schwager, “Scalable Distributed Optimization with Separable Variables in Multi-Agent Networks,” in 2020 American Control Conference (ACC), Jul. 2020, pp. 3619–3626. [pdf] [bibtex]
  5. Z. Wang, R. Spica, and M. Schwager, “Game Theoretic Motion Planning for Multi-robot Racing,” in Distributed Autonomous Robotic Systems, 2020, vol. 9, pp. 225–238. [pdf] [bibtex]
  6. R. Spica, E. Cristofalo, Z. Wang, E. Montijano, and M. Schwager, “A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing,” IEEE Transactions on Robotics, vol. 36, no. 5, pp. 1389–1403, 2020. [pdf] [bibtex]
  7. H. Nishimura, B. Ivanovic, A. Gaidon, M. Pavone, and M. Schwager, “Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 11205–11212. [pdf] [bibtex]
  8. G. Notomista, M. Wang, M. Schwager, and M. Egerstedt, “Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), Jun. 2020, p. 7. [pdf] [bibtex]
  9. P. Pueyo, E. Cristofalo, E. Montijano, and M. Schwager, “CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 1186–1191. [pdf] [bibtex]
  10. W. Wang, Z. Wang, L. Mateos, K. W. Huang, M. Schwager, C. Ratti, and D. Rus, “Distributed Motion Control for Multiple Connected Surface Vessels,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 11658–11665. [pdf] [bibtex]
  11. K. Brown, O. Peltzer, M. A. Sehr, M. Schwager, and M. J. Kochenderfer, “Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 441–447. [pdf] [bibtex]
  12. B. E. Jackson, T. A. Howell, K. Shah, M. Schwager, and Z. Manchester, “Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3368–3374, Apr. 2020. [pdf] [bibtex]
  13. N. S. Usevitch, Z. M. Hammond, M. Schwager, A. M. Okamura, E. W. Hawkes, and S. Follmer, “An untethered isoperimetric soft robot,” Science Robotics, vol. 5, no. 40, p. eaaz0492, Mar. 2020. [pdf] [bibtex]
  14. N. S. Usevitch, Z. M. Hammond, and M. Schwager, “Locomotion of Linear Actuator Robots Through Kinematic Planning and Nonlinear Optimization,” IEEE Transactions on Robotics, vol. 36, no. 5, pp. 1404–1421, Oct. 2020. [pdf] [bibtex]
  15. M. Wang, N. Mehr, A. Gaidon, and M. Schwager, “Game-Theoretic Planning for Risk-Aware Interactive Agents,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2020, pp. 6998–7005. [pdf] [bibtex]
  16. R. Senanayake, M. Toyungyernsub, M. Wang, M. J. Kochenderfer, and M. Schwager, “Directional primitives for uncertainty-aware motion estimation in urban environments,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. [pdf] [bibtex]
  17. K. Shah, G. Ballard, A. Schmidt, and M. Schwager, “Multidrone aerial surveys of penguin colonies in Antarctica,” Science Robotics, vol. 5, no. 47, p. eabc3000, Oct. 2020. [pdf] [bibtex]
  18. O. Shorinwa, J. Yu, T. Halsted, A. Koufos, and M. Schwager, “Distributed Multi-Target Tracking for Autonomous Vehicle Fleets,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), May 2020, pp. 3495–3501. [pdf] [bibtex]
  19. R. N. Haksar, S. Trimpe, and M. Schwager, “Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3027–3034, Apr. 2020. [pdf] [bibtex]
  20. S. Le Cleac’h, M. Schwager, and Z. Manchester, “ALGAMES: A Fast Solver for Constrained Dynamic Games,” in Robotics: Science and Systems XVI, Jul. 2020. [pdf] [bibtex]
  21. R. Madaan, N. Gyde, S. Vemprala, M. Brown, K. Nagami, T. Taubner, E. Cristofalo, D. Scaramuzza, M. Schwager, and A. Kapoor, “AirSim Drone Racing Lab,” in Proceedings of the NeurIPS 2019 Competition and Demonstration Track, Dec. 2020, vol. 123, pp. 177–191. [pdf] [bibtex]
  22. H. Nishimura and M. Schwager, “SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control,” in Algorithmic Foundations of Robotics XIII, 2020, vol. 14, pp. 267–283. [pdf] [bibtex]
  23. E. Cristofalo, E. Montijano, and M. Schwager, “Vision-Based Control for Fast 3-D Reconstruction With an Aerial Robot,” IEEE Transactions on Control Systems Technology, vol. 28, no. 4, pp. 1189–1202, Jul. 2020. [pdf] [bibtex]
  24. Z. Wang, T. Taubner, and M. Schwager, “Multi-agent sensitivity enhanced iterative best response: A real-time game theoretic planner for drone racing in 3D environments,” Robotics and Autonomous Systems, vol. 125, p. 103410, Mar. 2020. [pdf] [bibtex]

2019

  1. R. N. Haksar, O. Shorinwa, P. Washington, and M. Schwager, “Consensus-based ADMM for Task Assignment in Multi-Robot Teams,” in 2019 International Symposium on Robotics Research, Oct. 2019. [pdf] [bibtex]
  2. P. Culbertson, S. Bandyopadhyay, and M. Schwager, “Multi-Robot Assembly Sequencing via Discrete Optimization,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2019, pp. 6502–6509. [pdf] [bibtex]
  3. M. Wang, Z. Wang, J. Talbot, J. Christian Gerdes, and M. Schwager, “Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios,” in Robotics: Science and Systems XV, Jun. 2019. [pdf] [bibtex]
  4. G. Angeris, K. Shah, and M. Schwager, “Fast Reciprocal Collision Avoidance Under Measurement Uncertainty,” 2019. [pdf] [bibtex]
  5. A. Caccavale and M. Schwager, “Trust But Verify: A Distributed Algorithm for Multi-Robot Wireframe Exploration and Mapping,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 3294–3301. [pdf] [bibtex]
  6. K. Leahy and M. Schwager, “Tracking a Markov Target in a Discrete Environment With Multiple Sensors,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2396–2411, Jun. 2019. [pdf] [bibtex]
  7. J. Alonso-Mora, E. Montijano, T. Nägeli, O. Hilliges, M. Schwager, and D. Rus, “Distributed multi-robot formation control in dynamic environments,” Autonomous Robots, vol. 43, no. 5, pp. 1079–1100, Jun. 2019. [pdf] [bibtex]
  8. R. N. Haksar, J. Lorenzetti, and M. Schwager, “Scalable Filtering of Large Graph-Coupled Hidden Markov Models,” in 2019 IEEE 58th Conference on Decision and Control (CDC), Dec. 2019, pp. 1307–1314. [pdf] [bibtex]
  9. E. Cristofalo, E. Montijano, and M. Schwager, “Consensus-based Distributed 3D Pose Estimation with Noisy Relative Measurements,” in 2019 IEEE 58th Conference on Decision and Control (CDC), Dec. 2019, pp. 2646–2653. [pdf] [bibtex]
  10. K. Leahy, E. Cristofalo, C.-I. Vasile, A. Jones, E. Montijano, M. Schwager, and C. Belta, “Control in belief space with temporal logic specifications using vision-based localization,” The International Journal of Robotics Research, vol. 38, no. 6, pp. 702–722, May 2019. [pdf] [bibtex]
  11. K. Shah and M. Schwager, “GRAPE: Geometric Risk-Aware Pursuit-Evasion,” Robotics and Autonomous Systems, vol. 121, p. 103246, Nov. 2019. [pdf] [bibtex]
  12. K. Shah and M. Schwager, “Multi-agent Cooperative Pursuit-Evasion Strategies Under Uncertainty,” in Distributed Autonomous Robotic Systems, 2019, vol. 9, pp. 451–468. [pdf] [bibtex]
  13. R. N. Haksar, F. Solowjow, S. Trimpe, and M. Schwager, “Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources,” in 2019 IEEE 58th Conference on Decision and Control (CDC), Dec. 2019, pp. 1315–1322. [pdf] [bibtex]
  14. M. Wang and M. Schwager, “Distributed Collision Avoidance of Multiple Robots with Probabilistic Buffered Voronoi Cells,” in 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Aug. 2019, pp. 169–175. [pdf] [bibtex]

2018

  1. P. Culbertson and M. Schwager, “Decentralized Adaptive Control for Collaborative Manipulation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 278–285. [pdf] [bibtex]
  2. A. Pierson and M. Schwager, “Controlling Noncooperative Herds with Robotic Herders,” IEEE Transactions on Robotics, vol. 34, no. 2, pp. 517–525, Apr. 2018. [pdf] [bibtex]
  3. R. N. Haksar and M. Schwager, “Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1067–1074. [pdf] [bibtex]
  4. D. Zhou, Z. Wang, and M. Schwager, “Agile Coordination and Assistive Collision Avoidance for Quadrotor Swarms Using Virtual Structures,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 916–923, Aug. 2018. [pdf] [bibtex]
  5. H. Nishimura and M. Schwager, “Active Motion-Based Communication for Robots with Monocular Vision,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 2948–2955. [pdf] [bibtex]
  6. M. Wang, Z. Wang, S. Paudel, and M. Schwager, “Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 4678–4684. [pdf] [bibtex]
  7. R. N. Haksar and M. Schwager, “Controlling Large, Graph-based MDPs with Global Control Capacity Constraints: An Approximate LP Solution,” in 2018 IEEE Conference on Decision and Control (CDC), Dec. 2018, pp. 35–42. [pdf] [bibtex]
  8. Z. Wang, S. Singh, M. Pavone, and M. Schwager, “Cooperative Object Transport in 3D with Multiple Quadrotors Using No Peer Communication,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), May 2018, pp. 1064–1071. [pdf] [bibtex]
  9. Z. Wang, G. Yang, X. Su, and M. Schwager, “OuijaBots: Omnidirectional Robots for Cooperative Object Transport with Rotation Control Using No Communication,” in Distributed Autonomous Robotic Systems, 2018, vol. 6, pp. 117–131. [pdf] [bibtex]
  10. C.-I. Vasile, M. Schwager, and C. Belta, “Translational and Rotational Invariance in Networked Dynamical Systems,” IEEE Transactions on Control of Network Systems, vol. 5, no. 3, pp. 822–832, Sep. 2018. [pdf] [bibtex]
  11. A. Caccavale and M. Schwager, “Wireframe Mapping for Resource-Constrained Robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1–9. [pdf] [bibtex]

2017

  1. A. Pierson, L. C. Figueiredo, L. C. A. Pimenta, and M. Schwager, “Adapting to sensing and actuation variations in multi-robot coverage,” The International Journal of Robotics Research, vol. 36, no. 3, pp. 337–354, Mar. 2017. [pdf] [bibtex]
  2. N. Usevitch, Z. Hammond, S. Follmer, and M. Schwager, “Linear actuator robots: Differential kinematics, controllability, and algorithms for locomotion and shape morphing,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2017, pp. 5361–5367. [pdf] [bibtex]
  3. D. Zhou, Z. Wang, S. Bandyopadhyay, and M. Schwager, “Fast, On-line Collision Avoidance for Dynamic Vehicles Using Buffered Voronoi Cells,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 1047–1054, Apr. 2017. [pdf] [bibtex]
  4. T. Halsted and M. Schwager, “Distributed multi-robot localization from acoustic pulses using Euclidean distance geometry,” in 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Dec. 2017, pp. 104–111. [pdf] [bibtex]
  5. A. Pierson, Z. Wang, and M. Schwager, “Intercepting Rogue Robots: An Algorithm for Capturing Multiple Evaders With Multiple Pursuers,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 530–537, Apr. 2017. [pdf] [bibtex]
  6. X. Lan and M. Schwager, “Learning a dynamical system model for a spatiotemporal field using a mobile sensing robot,” in 2017 American Control Conference (ACC), May 2017, pp. 170–175. [pdf] [bibtex]
  7. E. Cristofalo, K. Leahy, C.-I. Vasile, E. Montijano, M. Schwager, and C. Belta, “Localization of a Ground Robot by Aerial Robots for GPS-Deprived Control with Temporal Logic Constraints,” in 2016 International Symposium on Experimental Robotics, 2017, vol. 1, pp. 525–537. [pdf] [bibtex]
  8. M. Schwager, M. P. Vitus, S. Powers, D. Rus, and C. J. Tomlin, “Robust Adaptive Coverage Control for Robotic Sensor Networks,” IEEE Transactions on Control of Network Systems, vol. 4, no. 3, pp. 462–476, Sep. 2017. [pdf] [bibtex]
  9. A. Caccavale and M. Schwager, “A distributed algorithm for mapping the graphical structure of complex environments with a swarm of robots,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 1459–1466. [pdf] [bibtex]

2016

  1. C.-I. Vasile, K. Leahy, E. Cristofalo, A. Jones, M. Schwager, and C. Belta, “Control in belief space with Temporal Logic specifications,” in 2016 IEEE 55th Conference on Decision and Control (CDC), Dec. 2016, pp. 7419–7424. [pdf] [bibtex]
  2. Ding, Huanyu, E. Cristofalo, J. Wang, D. Castanon, E. Montijano, V. Saligrama, and M. Schwager, “A multi-resolution approach for discovery and 3-D modeling of archaeological sites using satellite imagery and a UAV-borne camera,” in 2016 American Control Conference (ACC), Jul. 2016, pp. 1359–1365. [pdf] [bibtex]
  3. Z. Wang and M. Schwager, “Multi-robot Manipulation Without Communication,” in Distributed Autonomous Robotic Systems, 2016, vol. 112, pp. 135–149. [pdf] [bibtex]
  4. K. Leahy, D. Zhou, C.-I. Vasile, K. Oikonomopoulos, M. Schwager, and C. Belta, “Provably Correct Persistent Surveillance for Unmanned Aerial Vehicles Subject to Charging Constraints,” in Experimental Robotics, 2016, vol. 109, pp. 605–619. [pdf] [bibtex]
  5. J. Yu, M. Schwager, and D. Rus, “Correlated Orienteering Problem and its Application to Persistent Monitoring Tasks,” IEEE Transactions on Robotics, vol. 32, no. 5, pp. 1106–1118, Oct. 2016. [pdf] [bibtex]
  6. Z. Wang and M. Schwager, “Force-Amplifying N-robot Transport System (Force-ANTS) for cooperative planar manipulation without communication,” The International Journal of Robotics Research, vol. 35, no. 13, pp. 1564–1586, Nov. 2016. [pdf] [bibtex]
  7. K. Leahy, D. Zhou, C.-I. Vasile, K. Oikonomopoulos, M. Schwager, and C. Belta, “Persistent surveillance for unmanned aerial vehicles subject to charging and temporal logic constraints,” Autonomous Robots, vol. 40, no. 8, pp. 1363–1378, Dec. 2016. [pdf] [bibtex]
  8. A. Pierson and M. Schwager, “Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage,” in 2013 International Symposium of Robotics Research (ISRR), 2016, vol. 114, pp. 167–183. [pdf] [bibtex]
  9. P. M. Dames, M. Schwager, D. Rus, and V. Kumar, “Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 153–160, Jan. 2016. [pdf] [bibtex]
  10. K. Leahy and M. Schwager, “Always choose second best: Tracking a moving target on a graph with a noisy binary sensor,” in 2016 European Control Conference (ECC), Jun. 2016, pp. 1715–1721. [pdf] [bibtex]
  11. A. Pierson, A. Ataei, I. C. Paschalidis, and M. Schwager, “Cooperative multi-quadrotor pursuit of an evader in an environment with no-fly zones,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 320–326. [pdf] [bibtex]
  12. J. Alonso-Mora, E. Montijano, M. Schwager, and D. Rus, “Distributed Multi-Robot Navigation in Formation among Obstacles: A Geometric and Optimization Approach with Consensus,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, p. 8. [pdf] [bibtex]
  13. Z. Wang and M. Schwager, “Kinematic multi-robot manipulation with no communication using force feedback,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 427–432. [pdf] [bibtex]
  14. X. Lan and M. Schwager, “Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots,” IEEE Transactions on Robotics, vol. 32, no. 5, pp. 1230–1244, Oct. 2016. [pdf] [bibtex]
  15. D. Aksaray, A. Jones, Z. Kong, M. Schwager, and C. Belta, “Q-Learning for robust satisfaction of signal temporal logic specifications,” in 2016 IEEE 55th Conference on Decision and Control (CDC), Dec. 2016, pp. 6565–6570. [pdf] [bibtex]
  16. D. Zhou and M. Schwager, “Assistive collision avoidance for quadrotor swarm teleoperation,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 1249–1254. [pdf] [bibtex]
  17. R. Allen, M. Pavone, and M. Schwager, “Flying Smartphones: When Portable Computing Sprouts Wings,” IEEE Pervasive Computing, vol. 15, no. 3, pp. 83–88, Jul. 2016. [pdf] [bibtex]
  18. E. Montijano, E. Cristofalo, D. Zhou, M. Schwager, and C. Sagues, “Vision-Based Distributed Formation Control Without an External Positioning System,” IEEE Transactions on Robotics, vol. 32, no. 2, pp. 339–351, Apr. 2016. [pdf] [bibtex]
  19. E. Montijano, E. Cristofalo, M. Schwager, and C. Sagues, “Distributed formation control of non-holonomic robots without a global reference frame,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 5248–5254. [pdf] [bibtex]

2015

  1. A. Pierson, L. C. Figueiredo, L. C. A. Pimenta, and M. Schwager, “Adapting to performance variations in multi-robot coverage,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015, pp. 415–420. [pdf] [bibtex]
  2. D. Zhou and M. Schwager, “Virtual Rigid Bodies for coordinated agile maneuvering of teams of micro aerial vehicles,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015, pp. 1737–1742. [pdf] [bibtex]
  3. C.-I. Vasile, M. Schwager, and C. Belta, “SE(N) invariance in networked systems,” in 2015 European Control Conference (ECC), Jul. 2015, pp. 186–191. [pdf] [bibtex]
  4. G. Habibi, J. McLurkin, Z. Wang, Z. Kingston, and M. Schwager, “Pipelined Consensus for Global State Estimation in Multi-Agent Systems,” in 2015 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2015, pp. 1315–1323. [pdf] [bibtex]
  5. A. Pierson and M. Schwager, “Bio-inspired non-cooperative multi-robot herding,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015, pp. 1843–1849. [pdf] [bibtex]
  6. K. Leahy, A. Jones, M. Schwager, and C. Belta, “Distributed information gathering policies under temporal logic constraints,” in 2015 54th IEEE Conference on Decision and Control (CDC), Dec. 2015, pp. 6803–6808. [pdf] [bibtex]
  7. A. Jones, M. Schwager, and C. Belta, “Information-guided persistent monitoring under temporal logic constraints,” in 2015 American Control Conference (ACC), Jul. 2015, pp. 1911–1916. [pdf] [bibtex]

2014

  1. P. Dames, D. Thakur, M. Schwager, and V. Kumar, “Playing Fetch with Your Robot: The Ability of Robots to Locate and Interact with Objects,” IEEE Robotics & Automation Magazine, vol. 21, no. 2, pp. 46–52, Jun. 2014. [pdf] [bibtex]
  2. X. Lan and M. Schwager, “A variational approach to trajectory planning for persistent monitoring of spatiotemporal fields,” in 2014 American Control Conference, Jun. 2014, pp. 5627–5632. [pdf] [bibtex]
  3. D. Zhou and M. Schwager, “Vector field following for quadrotors using differential flatness,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), May 2014, pp. 6567–6572. [pdf] [bibtex]
  4. E. Montijano, D. Zhou, M. Schwager, and C. Sagues, “Distributed formation control without a global reference frame,” in 2014 American Control Conference, Jun. 2014, pp. 3862–3867. [pdf] [bibtex]
  5. D. E. Soltero, M. Schwager, and D. Rus, “Decentralized path planning for coverage tasks using gradient descent adaptive control,” The International Journal of Robotics Research, vol. 33, no. 3, pp. 401–425, Mar. 2014. [pdf] [bibtex]

2013

  1. S. L. Smith, M. Pavone, M. Schwager, E. Frazzoli, and D. Rus, “Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems,” in 2013 American Control Conference, Jun. 2013, pp. 2362–2367. [pdf] [bibtex]
  2. X. Lan and M. Schwager, “Planning periodic persistent monitoring trajectories for sensing robots in Gaussian Random Fields,” in 2013 IEEE International Conference on Robotics and Automation, May 2013, pp. 2415–2420. [pdf] [bibtex]
  3. A. Jones, M. Schwager, and C. Belta, “Distribution temporal logic: Combining correctness with quality of estimation,” in 52nd IEEE Conference on Decision and Control, Dec. 2013, pp. 4719–4724. [pdf] [bibtex]
  4. A. Jones, M. Schwager, and C. Belta, “A receding horizon algorithm for informative path planning with temporal logic constraints,” in 2013 IEEE International Conference on Robotics and Automation, May 2013, pp. 5019–5024. [pdf] [bibtex]

2012

  1. P. Dames, M. Schwager, V. Kumar, and D. Rus, “A decentralized control policy for adaptive information gathering in hazardous environments,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), Dec. 2012, pp. 2807–2813. [pdf] [bibtex]
  2. S. L. Smith, M. Schwager, and D. Rus, “Persistent Robotic Tasks: Monitoring and Sweeping in Changing Environments,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 410–426, Apr. 2012. [pdf] [bibtex]
  3. B. J. Julian, M. Angermann, M. Schwager, and D. Rus, “Distributed robotic sensor networks: An information-theoretic approach,” The International Journal of Robotics Research, vol. 31, no. 10, pp. 1134–1154, Sep. 2012. [pdf] [bibtex]
  4. D. E. Soltero, M. Schwager, and D. Rus, “Generating informative paths for persistent sensing in unknown environments,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2012, pp. 2172–2179. [pdf] [bibtex]

2011

  1. R. N. Smith, M. Schwager, S. L. Smith, B. H. Jones, D. Rus, and G. S. Sukhatme, “Persistent ocean monitoring with underwater gliders: Adapting sampling resolution: Persistent Ocean Monitoring with Underwater Gliders,” Journal of Field Robotics, vol. 28, no. 5, pp. 714–741, Sep. 2011. [pdf] [bibtex]
  2. M. Schwager, B. J. Julian, M. Angermann, and D. Rus, “Eyes in the Sky: Decentralized Control for the Deployment of Robotic Camera Networks,” Proceedings of the IEEE, vol. 99, no. 9, pp. 1541–1561, Sep. 2011. [pdf] [bibtex]
  3. M. Schwager, D. Rus, and J.-J. Slotine, “Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment,” The International Journal of Robotics Research, vol. 30, no. 3, pp. 371–383, Mar. 2011. [pdf] [bibtex]

2009

  1. M. Schwager, D. Rus, and J.-J. Slotine, “Decentralized, Adaptive Coverage Control for Networked Robots,” The International Journal of Robotics Research, vol. 28, no. 3, pp. 357–375, Mar. 2009. [pdf] [bibtex]

2008

  1. M. Schwager, C. Detweiler, I. Vasilescu, D. M. Anderson, and D. Rus, “Data-driven identification of group dynamics for motion prediction and control,” Journal of Field Robotics, vol. 25, no. 6-7, pp. 305–324, Jun. 2008. [pdf] [bibtex]

2007

  1. M. Schwager, D. M. Anderson, Z. Butler, and D. Rus, “Robust classification of animal tracking data,” Computers and Electronics in Agriculture, vol. 56, no. 1, pp. 46–59, Mar. 2007. [pdf] [bibtex]

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