Academic Appointments


Administrative Appointments


  • Associate Professor, Stanford University (2021 - Present)
  • Assistant Professor, Stanford University (2015 - 2021)
  • Assistant Professor, Boston University (2012 - 2015)
  • Postdoctoral Researcher, University of Pennsylvania (2010 - 2012)
  • Postdoctoral Researcher, MIT (2009 - 2011)
  • Automation Engineer, Applied Materials, Inc (2000 - 2002)

Honors & Awards


  • Best Paper Award in Multi-Robot Systems, International Conference on Robotics and Automation (ICRA) (2020)
  • Toshio Fukuda Young Professional Award, International Conference on Intelligent Robots and Systems (IROS) (2019)
  • Outstanding Professor Award in Aeronautics and Astronautics, AIAA Stanford Student Chapter (2018-2019)
  • Best Paper in Robot Manipulation, International Conference on Robotics and Automation (ICRA) (2018)
  • Google Faculty Research Award, Google (2018)
  • Young Faculty Award (YFA), DARPA (2018)
  • Best Paper Finalist, International Conference on Robotics and Automation (ICRA) (2016)
  • King-Sun Fu Memorial Best Paper Award, IEEE Transactions on Robotics (2016)
  • CAREER Award, NSF (2014)
  • Best Paper Finalist, International Conference on Robotics and Automation (ICRA) (2011)
  • Best Paper Award, Conference on the Simulation of Adaptive Behavior (SAB) (2008)
  • Best Paper Finalist, International Conference on Robotics and Automation (ICRA) (2008)

Professional Education


  • PhD, MIT, Mechanical Engineering (2009)
  • MS, MIT, Mechanical Engineering (2005)
  • BS, Stanford, Mechanical Engineering (2000)

All Publications


  • CoCo: Online Mixed-Integer Control Via Supervised Learning IEEE ROBOTICS AND AUTOMATION LETTERS Cauligi, A., Culbertson, P., Schmerling, E., Schwager, M., Stellato, B., Pavone, M. 2022; 7 (2): 1447-1454
  • DiNNO: Distributed Neural Network Optimization for Multi-Robot Collaborative Learning IEEE ROBOTICS AND AUTOMATION LETTERS Yu, J., Vincent, J. A., Schwager, M. 2022; 7 (2): 1896-1903
  • Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies IEEE TRANSACTIONS ON ROBOTICS Culbertson, P., Slotine, J., Schwager, M. 2021; 37 (6): 1906-1920
  • SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Nishimura, H., Schwager, M. 2021
  • Game-Theoretic Planning for Self-Driving Cars in Multivehicle Competitive Scenarios IEEE TRANSACTIONS ON ROBOTICS Wang, M., Wang, Z., Talbot, J., Gerdes, J., Schwager, M. 2021; 37 (4): 1313-1325
  • LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planning IEEE ROBOTICS AND AUTOMATION LETTERS Le Cleac'h, S., Schwager, M., Manchester, Z. 2021; 6 (3): 5485-5492
  • RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch IEEE ROBOTICS AND AUTOMATION LETTERS Nishimura, H., Mehr, N., Gaidon, A., Schwager, M. 2021; 6 (2): 763–70
  • HJB-RL:Initializing Reinforcement Learning with Optimal Control Policies Applied to Autonomous Drone Racing Nagami, K., Schwager, M., Shell, D. A., Toussaint, M., Hsieh, M. A. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2021
  • MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception International Symposium on Experimental Robotics Ramtoula, B., Caccavale, A., Beltrame, G., Schwager, M. 2021: 520-532
  • Reachable polyhedral marching (rpm): A safety verification algorithm for robotic systems with deep neural network components 2021 IEEE International Conference on Robotics and Automation (ICRA) Vincent, J. A., Schwager, M. IEEE. 2021: 9029-9035
  • TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Chen, C., Culbertson, P., Lepert, M., Schwager, M., Bohg, J. IEEE. 2021: 8262-8268
  • Learning Large Graph-based MDPs with Historical Data IEEE Transactions on Control of Network Systems ( Early Access ) Haksar, R. N., Schwager, M. 2021
  • Distributed Contact-Implicit Trajectory Optimization for Collaborative Manipulation 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) Shorinwa, O., Schwager, M. IEEE. 2021: 56-65
  • Multidrone aerial surveys of penguin colonies in Antarctica. Science robotics Shah, K., Ballard, G., Schmidt, A., Schwager, M. 2020; 5 (47)

    Abstract

    Speed is essential in wildlife surveys due to the dynamic movement of animals throughout their environment and potentially extreme changes in weather. In this work, we present a multirobot path-planning method for conducting aerial surveys over large areas designed to make the best use of limited flight time. Unlike current survey path-planning solutions based on geometric patterns or integer programs, we solve a series of satisfiability modulo theory instances of increasing complexity. Each instance yields a set of feasible paths at each iteration and recovers the set of shortest paths after sufficient time. We implemented our planning algorithm with a team of drones to conduct multiple photographic aerial wildlife surveys of Cape Crozier, one of the largest Adelie penguin colonies in the world containing more than 300,000 nesting pairs. Over 2 square kilometers was surveyed in about 3 hours. In contrast, previous human-piloted single-drone surveys of the same colony required over 2 days to complete. Our method reduces survey time by limiting redundant travel while also allowing for safe recall of the drones at any time during the survey. Our approach can be applied to other domains, such as wildfire surveys in high-risk weather conditions or disaster response.

    View details for DOI 10.1126/scirobotics.abc3000

    View details for PubMedID 33115884

  • Locomotion of Linear Actuator Robots Through Kinematic Planning and Nonlinear Optimization IEEE TRANSACTIONS ON ROBOTICS Usevitch, N. S., Hammond, Z. M., Schwager, M. 2020; 36 (5): 1404–21
  • A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing IEEE TRANSACTIONS ON ROBOTICS Spica, R., Cristofalo, E., Wang, Z., Montijano, E., Schwager, M. 2020; 36 (5): 1389–1403
  • Vision-Based Control for Fast 3-D Reconstruction With an Aerial Robot IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY Cristofalo, E., Montijano, E., Schwager, M. 2020; 28 (4): 1189–1202
  • Robots and autonomous systems, SI DARS 2018 ROBOTICS AND AUTONOMOUS SYSTEMS Correll, N., Schwager, M. 2020; 129
  • Scalable Cooperative Transport of Cable-Suspended Loads With UAVs Using Distributed Trajectory Optimization IEEE ROBOTICS AND AUTOMATION LETTERS Jackson, B. E., Howell, T. A., Shah, K., Schwager, M., Manchester, Z. 2020; 5 (2): 3368–74
  • Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage IEEE ROBOTICS AND AUTOMATION LETTERS Haksar, R. N., Trimpe, S., Schwager, M. 2020; 5 (2): 3027–34
  • An untethered isoperimetric soft robot SCIENCE ROBOTICS Usevitch, N. S., Hammond, Z. M., Schwager, M., Okamura, A. M., Hawkes, E. W., Follmer, S. 2020; 5 (40)
  • An untethered isoperimetric soft robot. Science robotics Usevitch, N. S., Hammond, Z. M., Schwager, M., Okamura, A. M., Hawkes, E. W., Follmer, S. 2020; 5 (40)

    Abstract

    For robots to be useful for real-world applications, they must be safe around humans, be adaptable to their environment, and operate in an untethered manner. Soft robots could potentially meet these requirements; however, existing soft robotic architectures are limited by their ability to scale to human sizes and operate at these scales without a tether to transmit power or pressurized air from an external source. Here, we report an untethered, inflated robotic truss, composed of thin-walled inflatable tubes, capable of shape change by continuously relocating its joints, while its total edge length remains constant. Specifically, a set of identical roller modules each pinch the tube to create an effective joint that separates two edges, and modules can be connected to form complex structures. Driving a roller module along a tube changes the overall shape, lengthening one edge and shortening another, while the total edge length and hence fluid volume remain constant. This isoperimetric behavior allows the robot to operate without compressing air or requiring a tether. Our concept brings together advantages from three distinct types of robots-soft, collective, and truss-based-while overcoming certain limitations of each. Our robots are robust and safe, like soft robots, but not limited by a tether; are modular, like collective robots, but not limited by complex subunits; and are shape-changing, like truss robots, but not limited by rigid linear actuators. We demonstrate two-dimensional (2D) robots capable of shape change and a human-scale 3D robot capable of punctuated rolling locomotion and manipulation, all constructed with the same modular rollers and operating without a tether.

    View details for DOI 10.1126/scirobotics.aaz0492

    View details for PubMedID 33022597

  • Multi-agent sensitivity enhanced iterative best response: A real-time game theoretic planner for drone racing in 3D environments ROBOTICS AND AUTONOMOUS SYSTEMS Wang, Z., Taubner, T., Schwager, M. 2020; 125
  • Scalable Distributed Optimization with Separable Variables in Multi-Agent Networks Shorinwa, O., Halsted, T., Schwager, M., IEEE IEEE. 2020: 3619–26
  • Distributed Multi-Target Tracking for Autonomous Vehicle Fleets Shorinwa, O., Yu, J., Halsted, T., Koufos, A., Mac Schwager, IEEE IEEE. 2020: 3495-3501
  • Enhancing Game-Theoretic Autonomous Car Racing Using Control Barrier Functions Notomista, G., Wang, M., Mac Schwager, Egerstedt, M., IEEE IEEE. 2020: 5393-5399
  • Optimal Sequential Task Assignment and Path Finding for Multi-Agent Robotic Assembly Planning Brown, K., Peltzer, O., Sehr, M. A., Schwager, M., Kochenderfer, M. J., IEEE IEEE. 2020: 441-447
  • Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control Cauligi, A., Culbertson, P., Stellato, B., Bertsimas, D., Mac Schwager, Pavone, M., IEEE IEEE. 2020: 1698-1705
  • Scalable Collaborative Manipulation with Distributed Trajectory Planning Shorinwa, O., Schwager, M., IEEE IEEE. 2020: 9108-9115
  • Game-Theoretic Planning for Risk-Aware Interactive Agents Wang, M., Mehr, N., Gaidon, A., Mac Schwager, IEEE IEEE. 2020: 6998-7005
  • Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction Nishimura, H., Ivanovic, B., Gaidon, A., Pavone, M., Schwager, M., IEEE IEEE. 2020: 11205-11212
  • Distributed Motion Control for Multiple Connected Surface Vessels Wang, W., Wang, Z., Mateos, L., Huang, K., Schwager, M., Ratti, C., Rus, D., IEEE IEEE. 2020: 11658-11665
  • Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments Senanayake, R., Toyungyernsub, M., Wang, M., Kochenderfer, M. J., Schwager, M., IEEE IEEE. 2020
  • CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes Pueyo, P., Cristofalo, E., Montijano, E., Mac Schwager, IEEE IEEE. 2020: 1186-1191
  • GRAPE: Geometric Risk-Aware Pursuit-Evasion ROBOTICS AND AUTONOMOUS SYSTEMS Shah, K., Schwager, M. 2019; 121
  • Tracking a Markov Target in a Discrete Environment With Multiple Sensors IEEE TRANSACTIONS ON AUTOMATIC CONTROL Leahy, K., Schwager, M. 2019; 64 (6): 2396–2411
  • Distributed multi-robot formation control in dynamic environments AUTONOMOUS ROBOTS Alonso-Mora, J., Montijano, E., Nageli, T., Hilliges, O., Schwager, M., Rus, D. 2019; 43 (5): 1079–1100
  • Control in belief space with temporal logic specifications using vision-based localization INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Leahy, K., Cristofalo, E., Vasile, C., Jones, A., Montijano, E., Schwager, M., Belta, C. 2019; 38 (6): 702–22
  • Multi-Robot Assembly Sequencing via Discrete Optimization Culbertson, P., Bandyopadhyay, S., Schwager, M., IEEE IEEE. 2019: 6502–9
  • Distributed Collision Avoidance of Multiple Robots with Probabilistic Buffered Voronoi Cells Wang, M., Schwager, M., Sabattini, L. IEEE. 2019: 169-175
  • Cooperative Control of an Autonomous Floating Modular Structure Without Communication EXTENDED ABSTRACT Wang, W., Mateos, L., Wang, Z., Huang, K., Schwager, M., Ratti, C., Rus, D., Sabattini, L. IEEE. 2019: 44-46
  • OuijaBots: Omnidirectional Robots for Cooperative Object Transport with Rotation Control Using No Communication Wang, Z., Yang, G., Su, X., Schwager, M., Gross, R., Kolling, A., Berman, S., Frazzoli, E., Martinoli, A., Matsuno, F., Gauci, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 117–31
  • Trust But Verify: A Distributed Algorithm for Multi-Robot Wireframe Exploration and Mapping Caccavale, A., Mac Schwager, IEEE IEEE. 2019: 3294–3301
  • Game Theoretic Planning for Self-Driving Cars in Competitive Scenarios Wang, M., Wang, Z., Talbot, J., Gerdes, J., Schwager, M., Bicchi, A., KressGazit, H., Hutchinson, S. MIT PRESS. 2019
  • Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources Haksar, R. N., Solowjow, F., Trimpe, S., Schwager, M., IEEE IEEE. 2019: 1315–22
  • Scalable Filtering of Large Graph-Coupled Hidden Markov Models Haksar, R. N., Lorenzetti, J., Schwager, M., IEEE IEEE. 2019: 1307–14
  • Consensus-based Distributed 3D Pose Estimation with Noisy Relative Measurements Cristofalo, E., Montijano, E., Schwager, M., IEEE IEEE. 2019: 2646–53
  • Translational and Rotational Invariance in Networked Dynamical Systems IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Vasile, C., Schwager, M., Belta, C. 2018; 5 (3): 822–32
  • Agile Coordination and Assistive Collision Avoidance for Quadrotor Swarms Using Virtual Structures IEEE TRANSACTIONS ON ROBOTICS Zhou, D., Wang, Z., Schwager, M. 2018; 34 (4): 916–23
  • Controlling Noncooperative Herds with Robotic Herders IEEE TRANSACTIONS ON ROBOTICS Pierson, A., Schwager, M. 2018; 34 (2): 517–25
  • Cooperative Object Transport in 3D with Multiple Quadrotors using No Peer Communication Wang, Z., Singh, S., Pavone, M., Schwager, M., IEEE IEEE COMPUTER SOC. 2018: 1064–71
  • A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing Spica, R., Falanga, D., Cristofalo, E., Montijano, E., Scaramuzza, D., Schwager, M., KressGazit, H., Srinivasa, S., Howard, T., Atanasov, N. MIT PRESS. 2018
  • Controlling Large, Graph-based MDPs with Global Control Capacity Constraints: An Approximate LP Solution Haksar, R. N., Schwager, M., IEEE IEEE. 2018: 35–42
  • Active Motion-Based Communication for Robots with Monocular Vision Nishimura, H., Schwager, M., IEEE IEEE COMPUTER SOC. 2018: 2948–55
  • Decentralized Adaptive Control for Collaborative Manipulation Culbertson, P., Schwager, M., IEEE IEEE COMPUTER SOC. 2018: 278–85
  • Safe Distributed Lane Change Maneuvers for Multiple Autonomous Vehicles Using Buffered Input Cells Wang, M., Wang, Z., Paudel, S., Schwager, M., IEEE IEEE COMPUTER SOC. 2018: 4678–84
  • Wireframe Mapping for Resource-Constrained Robots Caccavale, A., Schwager, M., Kosecka, J., Maciejewski, A. A., Okamura, A., Bicchi, A., Stachniss, C., Song, D. Z., Lee, D. H., Chaumette, F., Ding, H., Li, J. S., Wen, J., Roberts, J., Masamune, K., Chong, N. Y., Amato, N., Tsagwarakis, N., Rocco, P., Asfour, T., Chung, W. K., Yasuyoshi, Y., Sun, Y., Maciekeski, T., Althoefer, K., AndradeCetto, J., Chung, W. K., Demircan, E., Dias, J., Fraisse, P., Gross, R., Harada, H., Hasegawa, Y., Hayashibe, M., Kiguchi, K., Kim, K., Kroeger, T., Li, Y., Ma, S., Mochiyama, H., Monje, C. A., Rekleitis, Roberts, R., Stulp, F., Tsai, C. H., Zollo, L. IEEE. 2018: 8658–65
  • Distributed Deep Reinforcement Learning for Fighting Forest Fires with a Network of Aerial Robots Haksar, R. N., Schwager, M., Kosecka, J., Maciejewski, A. A., Okamura, A., Bicchi, A., Stachniss, C., Song, D. Z., Lee, D. H., Chaumette, F., Ding, H., Li, J. S., Wen, J., Roberts, J., Masamune, K., Chong, N. Y., Amato, N., Tsagwarakis, N., Rocco, P., Asfour, T., Chung, W. K., Yasuyoshi, Y., Sun, Y., Maciekeski, T., Althoefer, K., AndradeCetto, J., Chung, W. K., Demircan, E., Dias, J., Fraisse, P., Gross, R., Harada, H., Hasegawa, Y., Hayashibe, M., Kiguchi, K., Kim, K., Kroeger, T., Li, Y., Ma, S., Mochiyama, H., Monje, C. A., Rekleitis, Roberts, R., Stulp, F., Tsai, C. H., Zollo, L. IEEE. 2018: 1067–74
  • Robust Adaptive Coverage Control for Robotic Sensor Networks IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Schwager, M., Vitus, M. P., Powers, S., Rus, D., Tomlin, C. J. 2017; 4 (3): 462-476
  • Fast, On-line Collision Avoidance for Dynamic Vehicles Using Buffered Voronoi Cells IEEE ROBOTICS AND AUTOMATION LETTERS Zhou, D., Wang, Z., Bandyopadhyay, S., Schwager, M. 2017; 2 (2): 1047–54
  • Intercepting Rogue Robots: An Algorithm for Capturing Multiple Evaders With Multiple Pursuers IEEE ROBOTICS AND AUTOMATION LETTERS Pierson, A., Wang, Z., Schwager, M. 2017; 2 (2): 530–37
  • Adapting to sensing and actuation variations in multi-robot coverage INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Pierson, A., Figueiredo, L. C., Pimenta, L. C., Schwager, M. 2017; 36 (3): 337-354
  • Localization of a Ground Robot by Aerial Robots for GPS-Deprived Control with Temporal Logic Constraints Cristofalo, E., Leahy, K., Vasile, C., Montijano, E., Schwager, M., Belta, C., Kulic, D., Nakamura, Y., Khatib, O., Venture, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 525–37
  • Robust Adaptive Coverage for Robotic Sensor Networks Schwager, M., Vitus, M. P., Rus, D., Tomlin, C. J., Christensen, H. I., Khatib, O. SPRINGER-VERLAG BERLIN. 2017
  • Distributed Multi-Robot Localization from Acoustic Pulses Using Euclidean Distance Geometry Halsted, T., Schwager, M., Giordano, P. R. IEEE. 2017
  • Learning a Dynamical System Model for a Spatiotemporal Field Using a Mobile Sensing Robot Lan, X., Schwager, M., IEEE IEEE. 2017: 170–75
  • Force-Amplifying N-robot Transport System (Force-ANTS) for cooperative planar manipulation without communication INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Wang, Z., Schwager, M. 2016; 35 (13): 1564-1586
  • Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots IEEE TRANSACTIONS ON ROBOTICS Lan, X., Schwager, M. 2016; 32 (5): 1230-1244
  • Correlated Orienteering Problem and its Application to Persistent Monitoring Tasks IEEE TRANSACTIONS ON ROBOTICS Yu, J., Schwager, M., Rus, D. 2016; 32 (5): 1106-1118
  • Flying Smartphones: When Portable Computing Sprouts Wings IEEE PERVASIVE COMPUTING Allen, R., Pavone, M., Schwager, M. 2016; 15 (3): 83-88
  • Vision-Based Distributed Formation Control Without an External Positioning System IEEE TRANSACTIONS ON ROBOTICS Montijano, E., Cristofalo, E., Zhou, D., Schwager, M., Saguees, C. 2016; 32 (2): 339-351
  • Distributed Multi-Robot Formation Control among Obstacles: A Geometric and Optimization Approach with Consensus Alonso-Mora, J., Montijano, E., Schwager, M., Rus, D., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 5356-5363
  • Cooperative Multi-Quadrotor Pursuit of an Evader in an Environment with No-Fly Zones Pierson, A., Ataei, A., Paschalidis, I., Schwager, M., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 320-326
  • Distributed Formation Control of Non-Holonomic Robots without a Global Reference Frame Montijano, E., Cristofalo, E., Schwager, M., Sagues, C., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 5248-5254
  • Assistive Collision Avoidance for Quadrotor Swarm Teleoperation Zhou, D., Schwager, M., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 1249-1254
  • A Multi-Resolution Approach for Discovery and 3-D Modeling of Archaeological Sites Using Satellite Imagery and a UAV-borne Camera Ding, H., Cristofalo, E., Wang, J., Castanon, D., Montijano, E., Saligrama, V., Schwager, M., IEEE IEEE. 2016: 1359-1365
  • Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage Pierson, A., Schwager, M., Inaba, M., Corke, P. SPRINGER-VERLAG BERLIN. 2016: 167-183
  • Q-Learning for Robust Satisfaction of Signal Temporal Logic Specifications Aksaray, D., Jones, A., Kong, Z., Schwager, M., Belta, C., IEEE IEEE. 2016: 6565-6570
  • Control in Belief Space with Temporal Logic Specifications Vasile, C., Leahy, K., Cristofalo, E., Jones, A., Schwager, M., Belta, C., IEEE IEEE. 2016: 7419-7424
  • Always Choose Second Best: Tracking a Moving Target on a Graph with a Noisy Binary Sensor Leahy, K., Schwager, M., IEEE IEEE. 2016: 1715-1721
  • Kinematic Multi-Robot Manipulation with no Communication Using Force Feedback Wang, Z., Schwager, M., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 427–32
  • Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles IEEE ROBOTICS AND AUTOMATION LETTERS Dames, P. M., Schwager, M., Rus, D., Kumar, V. 2016; 1 (1): 153–60
  • Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems American Control Conference (ACC) Smith, S. L., Pavone, M., Schwager, M., Frazzoli, E., Rus, D. IEEE. 2013: 2362–2367