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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Collaborative Manipulation

  1. 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]
  2. 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]
  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. 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]
  5. 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]

Game Theoretic Planning

  1. S. Le Cleac’h, M. Schwager, and Z. Manchester, “ALGAMES: A Fast Augmented Lagrangian Solver for Constrained Dynamic Games,” Autonomous Robots, 2021. [pdf] [bibtex]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]

Cooperative Planning

  1. 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]
  2. 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]
  3. 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]
  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. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]

Formation Control

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]

Persistent Surveillance

  1. 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]
  2. 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]
  3. 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]
  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]

Coverage Control

  1. 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]
  2. 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]
  3. 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]
  4. 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]

Signal Temporal Logic

  1. 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]
  2. 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]
  3. 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]

Pursuit Evasion

  1. K. Shah and M. Schwager, “GRAPE: Geometric Risk-Aware Pursuit-Evasion,” Robotics and Autonomous Systems, vol. 121, p. 103246, Nov. 2019. [pdf] [bibtex]
  2. 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]
  3. 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]
  4. 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]

Soft Robotics

  1. 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]

Belief Space Control

  1. 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]

Optimal Control

  1. 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. [bibtex]
  2. S. Le Cleac’h, T. A. Howell, M. Schwager, and Z. Manchester, “Fast Contact-Implicit Model-Predictive Control,” Transactions on Robotics, Sep. 2021. Under Review. [bibtex]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
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