Bio


Dr. Marco Pavone is an Assistant Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of several awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an ONR Young Investigator Award, an NSF CAREER Award, and a NASA Early Career Faculty Award. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at the International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, and at NASA symposia.

Academic Appointments


Honors & Awards


  • PECASE Award, White House (2017)
  • YIP Award, ONR (2017)
  • CAREER Award, NSF (2015)
  • Frontiers of Engineering Program, National Academy of Engineering (2013)
  • Early Career Faculty award, NASA (2012)
  • Hellman Faculty Scholar Award, Hellman Fellows Fund (2012)
  • NIAC Fellow, NASA (2011)

Program Affiliations


  • Institute for Computational and Mathematical Engineering (ICME)
  • Stanford SystemX Alliance

Professional Education


  • Ph.D., MIT, Aeronautics and Astronautics (2010)

All Publications


  • An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning. Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems Starek, J. A., Gomez, J. V., Schmerling, E., Janson, L., Moreno, L., Pavone, M. ; 2015: 2072–78

    Abstract

    Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bidirectional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.

    View details for PubMedID 27004130

    View details for PubMedCentralID PMC4797999

  • A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms IEEE TRANSACTIONS ON AUTOMATIC CONTROL Singh, S., Chow, Y., Majumdar, A., Pavone, M. 2019; 64 (7): 2905–12
  • Robot Motion Planning in Learned Latent Spaces IEEE ROBOTICS AND AUTOMATION LETTERS Ichter, B., Pavone, M. 2019; 4 (3): 2407–14
  • A real-time framework for kinodynamic planning in dynamic environments with application to quadrotor obstacle avoidance ROBOTICS AND AUTONOMOUS SYSTEMS Allen, R. E., Pavone, M. 2019; 115: 174–93
  • A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Iglesias, R., Rossi, F., Zhang, R., Pavone, M. 2019; 38 (2-3): 357–74
  • Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality Abtahi, P., Landry, B., Yang, J., Pavone, M., Follmer, S., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Singh, S., Lacotte, J., Majumdar, A., Pavone, M. 2018; 37 (13-14): 1713–40
  • Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms Rossi, F., Zhang, R., Hindy, Y., Pavone, M. SPRINGER. 2018: 1427–42
  • The Team Surviving Orienteers problem: routing teams of robots in uncertain environments with survival constraints AUTONOMOUS ROBOTS Jorgensen, S., Chen, R. H., Milam, M. B., Pavone, M. 2018; 42 (4): 927–52
  • Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty Janson, L., Schmerling, E., Pavone, M., Bicchi, A., Burgard, W. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 343–61
  • Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming Lorenzetti, J., Chen, M., Landry, B., Pavone, M., IEEE IEEE. 2018: 4409–16
  • Stochastic Model Predictive Control for Autonomous Mobility on Demand Tsao, M., Iglesias, R., Pavone, M., IEEE IEEE. 2018: 3941–48
  • 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
  • Learning Sampling Distributions for Robot Motion Planning Ichter, B., Harrison, J., Pavone, M., IEEE IEEE COMPUTER SOC. 2018: 7087–94
  • Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction Schmerling, E., Leung, K., Vollprecht, W., Pavone, M., IEEE IEEE COMPUTER SOC. 2018: 3399–3406
  • Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems Iglesias, R., Rossi, F., Wang, K., Hallac, D., Leskovec, J., Pavone, M., IEEE IEEE COMPUTER SOC. 2018: 6019–25
  • Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming Landry, B., Chen, M., Hemley, S., Pavone, 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: 4325–32
  • Generative Modeling of Multimodal Multi-Human Behavior Ivanovic, B., Schmerling, E., Leung, K., Pavone, 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: 3088–95
  • Gravimetric Localization on the Surface of Small Bodies Hockman, B., Reid, R. G., Nesnas, I. D., Pavone, M., IEEE IEEE. 2018
  • Risk-Constrained Reinforcement Learning with Percentile Risk Criteria JOURNAL OF MACHINE LEARNING RESEARCH Chow, Y., Ghavamzadeh, M., Janson, L., Pavone, M. 2018; 18
  • Deterministic sampling-based motion planning: Optimality, complexity, and performance INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Janson, L., Ichter, B., Pavone, M. 2018; 37 (1): 46–61
  • Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance Janson, L., Ichter, B., Pavone, M., Bicchi, A., Burgard, W. SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 507–25
  • Fast, Safe, Propellant-Efficient Spacecraft Motion Planning Under Clohessy-Wiltshire-Hill Dynamics JOURNAL OF GUIDANCE CONTROL AND DYNAMICS Starek, J. A., Schmerling, E., Maher, G. D., Barbee, B. W., Pavone, M. 2017; 40 (2): 418-438

    View details for DOI 10.2514/1.G001913

    View details for Web of Science ID 000395514600018

  • Design, Control, and Experimentation of Internally-Actuated Rovers for the Exploration of Low-gravity Planetary Bodies JOURNAL OF FIELD ROBOTICS Hockman, B. J., Frick, A., Reid, R. G., Nesnas, I. A., Pavone, M. 2017; 34 (1): 5-24

    View details for DOI 10.1002/rob.21656

    View details for Web of Science ID 000393671700002

  • Experimental Methods for Mobility and Surface Operations of Microgravity Robots Hockman, B., Reid, R. G., Nesnas, I. D., Pavone, M., Kulic, D., Nakamura, Y., Khatib, O., Venture, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 752–63
  • Extreme Engineering: Extreme Autonomy in Space and Air, on Land, and Under Water Jackson, D., Pavone, M., Natl Acad Engn NATL ACADEMIES PRESS. 2017: 31–32
  • The Matroid Team Surviving Orienteers Problem: Constrained Routing of Heterogeneous Teams with Risky Traversal Jorgensen, S., Chen, R. H., Milam, M. B., Pavone, M., Bicchi, A., Okamura, A. IEEE. 2017: 5622–29
  • The Risk-Sensitive Coverage Problem: Multi-Robot Routing Under Uncertainty with Service Level and Survival Constraints Jorgensen, S., Chen, R. H., Milam, M. B., Pavone, M., IEEE IEEE. 2017
  • Control of robotic mobility-on-demand systems: A queueing-theoretical perspective INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Zhang, R., Pavone, M. 2016; 35 (1-3): 186-203
  • Chance-constrained dynamic programming with application to risk-aware robotic space exploration AUTONOMOUS ROBOTS Ono, M., Pavone, M., Kuwata, Y., Balaram, J. 2015; 39 (4): 555-571
  • Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics. Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control Schmerling, E., Janson, L., Pavone, M. 2015; 2015: 2574-2581

    Abstract

    In this paper we provide a thorough, rigorous theoretical framework to assess optimality guarantees of sampling-based algorithms for drift control systems: systems that, loosely speaking, can not stop instantaneously due to momentum. We exploit this framework to design and analyze a sampling-based algorithm (the Differential Fast Marching Tree algorithm) that is asymptotically optimal, that is, it is guaranteed to converge, as the number of samples increases, to an optimal solution. In addition, our approach allows us to provide concrete bounds on the rate of this convergence. The focus of this paper is on mixed time/control energy cost functions and on linear affine dynamical systems, which encompass a range of models of interest to applications (e.g., double-integrators) and represent a necessary step to design, via successive linearization, sampling-based and provably-correct algorithms for non-linear drift control systems. Our analysis relies on an original perturbation analysis for two-point boundary value problems, which could be of independent interest.

    View details for PubMedID 26997749

  • Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Janson, L., Schmerling, E., Clark, A., Pavone, M. 2015; 34 (7): 883-921
  • Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms With Robust Performance Constraints JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME Chow, Y., Pavone, M., Sadler, B. M., Carpin, S. 2015; 137 (3)

    View details for DOI 10.1115/1.4028117

    View details for Web of Science ID 000349754500007

  • Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions. The International journal of robotics research Janson, L., Schmerling, E., Clark, A., Pavone, M. 2015; 34 (7): 883–921

    Abstract

    In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a "lazy" dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds-the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n-1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.

    View details for PubMedID 27003958

    View details for PubMedCentralID PMC4798023

  • Optimal Sampling-Based Motion Planning under Differential Constraints: the Driftless Case. IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation Schmerling, E., Janson, L., Pavone, M. 2015; 2015: 2368–75

    Abstract

    Motion planning under differential constraints is a classic problem in robotics. To date, the state of the art is represented by sampling-based techniques, with the Rapidly-exploring Random Tree algorithm as a leading example. Yet, the problem is still open in many aspects, including guarantees on the quality of the obtained solution. In this paper we provide a thorough theoretical framework to assess optimality guarantees of sampling-based algorithms for planning under differential constraints. We exploit this framework to design and analyze two novel sampling-based algorithms that are guaranteed to converge, as the number of samples increases, to an optimal solution (namely, the Differential Probabilistic RoadMap algorithm and the Differential Fast Marching Tree algorithm). Our focus is on driftless control-affine dynamical models, which accurately model a large class of robotic systems. In this paper we use the notion of convergence in probability (as opposed to convergence almost surely): the extra mathematical flexibility of this approach yields convergence rate bounds - a first in the field of optimal sampling-based motion planning under differential constraints. Numerical experiments corroborating our theoretical results are presented and discussed.

    View details for PubMedID 26618041

    View details for PubMedCentralID PMC4659485

  • A Unifying Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms Chow, Y., L., Pavone, M. 2014
  • A Machine Learning Approach for Real-Time Reachability Analysis IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Allen, R. E., Clark, A. A., Starek, J. A., Pavone, M. IEEE. 2014: 2202–2208
  • A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms American Control Conference Chow, Y., Pavone, M. IEEE. 2014: 4204–4211
  • Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore 2nd Annual Workshop on Road Vehicle Automation Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D., Pavone, M. SPRINGER INT PUBLISHING AG. 2014: 229–245
  • Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems IEEE TRANSACTIONS ON AUTOMATIC CONTROL Treleaven, K., Pavone, M., Frazzoli, E. 2013; 58 (9): 2261-2276
  • Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies IEEE Aerospace Conference Pavone, M., Castillo-Rogez, J. C., Nesnas, I. A., Hoffman, J. A., Strange, N. J. IEEE. 2013
  • Decentralized decision-making on robotic networks with hybrid performance metrics 51st Annual Allerton Conference on Communication, Control, and Computing Rossi, F., Pavone, M. IEEE. 2013: 358–365
  • Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation IEEE International Conference on Robotics and Automation (ICRA) Allen, R., Pavone, M., McQuin, C., Nesnas, I. A., Castillo-Rogez, J. C., Tam-Nguyen Nguyen, T. N., Hoffman, J. A. IEEE. 2013: 5481–5488
  • Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation Allen, R., Pavone, M., McQuin, C., Nesnas, I., Castillo, J., Nguyen, T., N. 2013
  • Guidance, Navigation, and Control Technology Assessment for Future Planetary Science Missions. Technical Report for Planetary Science Division, Science Mission Directorate, NASA Quadrelli, M., McHenry, M., Wilcox, B., Hall, J., Volpe, R., Nesnas, I., Pavone, M. 2013
  • Decentralized decision-making on robotic networks with hybrid performance metrics Rossi, F., Pavone, M. 2013
  • A Uniform-grid Discretization Algorithm for Stochastic Optimal Control with Risk Constraints Chow, Y., L., Pavone, M. 2013
  • Asymptotically Optimal Algorithms for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems IEEE Transactions on Automatic Control Treleaven, K., Pavone, M., Frazzoli, E. 2013
  • Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems Smith, S., L., Pavone, M., Schwager, M., Frazzol, E., Rus, D. 2013
  • Stochastic Optimal Control With Dynamic, Time-Consistent Risk Constraints Chow, Y., L., Pavone, M. 2013
  • Stochastic Optimal Control With Dynamic, Time-Consistent Risk Constraints American Control Conference (ACC) Chow, Y., Pavone, M. IEEE. 2013: 390–395
  • 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
  • Robotic load balancing for mobility-on-demand systems INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Pavone, M., Smith, S. L., Frazzoli, E., Rus, D. 2012; 31 (7): 839-854
  • Cost Bounds for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems American Control Conference (ACC) Treleaven, K., Pavone, M., Frazzoli, E. IEEE COMPUTER SOC. 2012: 2120–2127
  • Models and Asymptotically Optimal Algorithms for Pickup and Delivery Problems on Roadmaps Treleaven, K., Pavone, M., Frazzoli, E. 2012
  • Observational Strategies for the Exploration of Small Solar System Bodies Castillo, M., Pavone, M., Nesnas, I., Hoffman, J. 2012
  • A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Mars Missions Kuwata, Y., Pavone, M., Balaram, J. 2012
  • Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies. Final Report for NASA NIAC 2011 Program. Pavone, M., Castillo, J., Hoffman, J., Nesnas, I. 2012
  • A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Planetary Missions 51st IEEE Annual Conference on Decision and Control (CDC) Kuwata, Y., Pavone, M., Balaram, J. (. IEEE. 2012: 2102–2109
  • Models and Efficient Algorithms for Pickup and Delivery Problems on Roadmaps 51st IEEE Annual Conference on Decision and Control (CDC) Treleaven, K., Pavone, M., Frazzoli, E. IEEE. 2012: 5691–5698
  • Dynamic Vehicle Routing for Robotic Systems PROCEEDINGS OF THE IEEE Bullo, F., Frazzoli, E., Pavone, M., Savla, K., Smith, S. L. 2011; 99 (9): 1482-1504
  • Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks IEEE TRANSACTIONS ON AUTOMATIC CONTROL Pavone, M., Arsie, A., Frazzoli, E., Bullo, F. 2011; 56 (8): 1834-1848
  • Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment IEEE TRANSACTIONS ON AUTOMATIC CONTROL Pavone, M., Frazzoli, E., Bullo, F. 2011; 56 (6): 1259-1274
  • An Asymptotically Optimal Algorithm for Pickup and Delivery Problems 50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC) Treleaven, K., Pavone, M., Frazzoli, E. IEEE. 2011: 584–590
  • Load Balancing for Mobility-on-Demand Systems Pavone, M., Smith, S., L., Frazzoli, E., Rus, D. 2011
  • Distributed Control of Spacecraft Formations via Cyclic Pursuit: Theory and Experiments JOURNAL OF GUIDANCE CONTROL AND DYNAMICS Ramirez-Riberos, J. L., Pavone, M., Frazzoli, E., Mille, D. W. 2010; 33 (5): 1655-1669

    View details for DOI 10.2514/1.46511

    View details for Web of Science ID 000282073600030

  • DYNAMIC VEHICLE ROUTING WITH PRIORITY CLASSES OF STOCHASTIC DEMANDS SIAM JOURNAL ON CONTROL AND OPTIMIZATION Smith, S. L., Pavone, M., Bullo, F., Frazzoli, E. 2010; 48 (5): 3224-3245

    View details for DOI 10.1137/090749347

    View details for Web of Science ID 000277585500002

  • Fundamental Performance Limits and Efficient Policies for Transportation-On-Demand Systems Pavone, M., Treleaven, K., Frazzoli, E. 2010
  • Dynamic Vehicle Routing with Stochastic Time Constraints IEEE International Conference on Robotics and Automation (ICRA) Pavone, M., Frazzoli, E. IEEE. 2010: 1460–1467
  • Fundamental Performance Limits and Efficient Polices for Transportation-On-Demand Systems 49th IEEE Conference on Decision and Control (CDC) Pavone, M., Treleaven, K., Frazzoli, E. IEEE. 2010: 5622–5629
  • A Stochastic and Dynamic Vehicle Routing Problem with Time Windows and Customer Impatience 1st International Conference on Robot Communication and Coordination (ROBOCOMM 2007) Pavone, M., Bisnik, N., Frazzoli, E., Isler, V. SPRINGER. 2009: 350–64
  • Sharing the Load Mobile Robotic Networks in Dynamic Environments IEEE ROBOTICS & AUTOMATION MAGAZINE Pavone, M., Savla, K., Frazzoli, E. 2009; 16 (2): 52-61
  • Equitable Partitioning Policies for Robotic Networks IEEE International Conference on Robotics and Automation Pavone, M., Arsie, A., Frazzoli, E., Bullo, F. IEEE. 2009: 3979–3984
  • Sharing the load IEEE Robotics & Automation Magazine Pavone, M., Savla, K., Frazzoli, E. 2009; 16 (2): 52-61
  • Distributed Control of Spacecraft Formation via Cyclic Pursuit: Theory and Experiments American Control Conference 2009 Ramirez, J. L., Pavone, M., Frazzoli, E., Miller, D. W. IEEE. 2009: 4811–4817
  • Dynamic Multi-Vehicle Routing with Multiple Classes of Demands American Control Conference 2009 Pavone, M., Smith, S. L., Bullo, F., Frazzoli, E. IEEE. 2009: 604–609
  • Distributed Policies for Equitable Partitioning: Theory and Applications 47th IEEE Conference on Decision and Control Pavone, M., Frazzoli, E., Bullo, F. IEEE. 2008: 4191–4197
  • Dynamic vehicle routing with heterogeneous demands Smith, S., L., Pavone, M., Bullo, F., Frazzoli, E. 2008
  • Decentralized policies for geometric pattern formation and path coverage JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME Pavone, M., Frazzoli, E. 2007; 129 (5): 633-643

    View details for DOI 10.1115/1.2767658

    View details for Web of Science ID 000249705300007

  • Decentralized policies for geometric pattern formation 26th American Control Conference Pavone, M., Frazzoli, E. IEEE. 2007: 5823–5828
  • Decentralized algorithms for stochastic and dynamic vehicle routing with general demand distribution Pavone, M., Frazzoli, E., Bullo, F. 2007
  • Decentralized Vehicle Routing in a Stochastic and Dynamic Environment with Customer Impatience Pavone, M., N., B., Frazzoli, E., Isler, V. 2007
  • Climbing Obstacle in Bio-robots via CNN and Adaptive Attitude Control International Journal of Circuit Theory and Applications Pavone, M., Arena, P., Fortuna, L., Frasca, M., Patanè, L. 2006; 34 (1): 109-125
  • An innovative mechanical and control architecture for a biomimetic hexapod for planetary exploration Space Technology Pavone, M., Arena, P., Patanè, L. 2006; 26 (1-2): 13-24
  • Realization of a CNN-Driven Cockroach-Inspired Robot Arena, P., Fortuna, L., Frasca, M., Patanè, L. 2006
  • Towards autonomous adaptive behavior in a bio-inspired CNN-controlled robot Arena, P., Fortuna, L., Frasca, M., Patanè, L., Pavone, M. 2006
  • An innovative mechanical and control architecture for a biomimetic hexapod for planetary exploration Pavone, M., Arena, P., Patanè, L. 2005
  • Climbing Obstacles via Bio-Inspired CNN-CPG and Adaptive Attitude Control Arena, P., Fortuna, L., Frasca, M., Patanè, L., Pavone, M. 2005