Bio


Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He is also a Distinguished Research Scientist at NVIDIA where he leads autonomous vehicle research. 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 a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. 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 a number of venues, including the European Conference on Computer Vision, the IEEE International Conference on Robotics and Automation, the European Control Conference, the IEEE International Conference on Intelligent Transportation Systems, the Field and Service Robotics Conference, the Robotics: Science and Systems Conference, and the INFORMS Annual Meeting.

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


Professional Education


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

2023-24 Courses


Stanford Advisees


All Publications


  • Bayesian Embeddings for Few-Shot Open World Recognition. IEEE transactions on pattern analysis and machine intelligence Willes, J., Harrison, J., Harakeh, A., Finn, C., Pavone, M., Waslander, S. L. 2024; 46 (3): 1513-1529

    Abstract

    As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information. This stands in stark contrast to modern machine learning systems that are typically designed with a known set of classes and a large number of examples for each class. In this work we extend embedding-based few-shot learning algorithms to the open-world recognition setting. We combine Bayesian non-parametric class priors with an embedding-based pre-training scheme to yield a highly flexible framework which we refer to as few-shot learning for open world recognition (FLOWR). We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets. Our results show, compared to prior methods, strong classification accuracy performance and up to a 12% improvement in H-measure (a measure of novel class detection) from our non-parametric open-world few-shot learning scheme.

    View details for DOI 10.1109/TPAMI.2022.3201541

    View details for PubMedID 36063507

  • Interactive Joint Planning for Autonomous Vehicles IEEE ROBOTICS AND AUTOMATION LETTERS Chen, Y., Veer, S., Karkus, P., Pavone, M. 2024; 9 (2): 987-994
  • Risk-Averse Trajectory Optimization via Sample Average Approximation IEEE ROBOTICS AND AUTOMATION LETTERS Lew, T., Bonalli, R., Pavone, M. 2024; 9 (2): 1500-1507
  • Dynamic Locational Marginal Emissions via Implicit Differentiation IEEE TRANSACTIONS ON POWER SYSTEMS Valenzuela, L., Degleris, A., El Gamal, A., Pavone, M., Rajagopal, R. 2024; 39 (1): 1138-1147
  • Sample-efficient safety assurances using conformal prediction INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Luo, R., Zhao, S., Kuck, J., Ivanovic, B., Savarese, S., Schmerling, E., Pavone, M. 2023
  • The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Jorgensen, S., Pavone, M. 2023
  • Semantic anomaly detection with large language models AUTONOMOUS ROBOTS Elhafsi, A., Sinha, R., Agia, C., Schmerling, E., Nesnas, I. D., Pavone, M. 2023
  • Near-Optimal Multi-Robot Motion Planning with Finite Sampling IEEE TRANSACTIONS ON ROBOTICS Dayan, D., Solovey, K., Pavone, M., Halperin, D. 2023; 39 (5): 3422-3436
  • Balancing fairness and efficiency in traffic routing via interpolated traffic assignment AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS Jalota, D., Solovey, K., Tsao, M., Zoepf, S., Pavone, M. 2023; 37 (2)
  • Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control IEEE TRANSACTIONS ON AUTOMATIC CONTROL Bonalli, R., Lew, T., Pavone, M. 2023; 68 (8): 4570-4585
  • Robust feedback motion planning via contraction theory INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Singh, S., Landry, B., Majumdar, A., Slotine, J., Pavone, M. 2023; 42 (9): 655-688
  • Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms GAMES AND ECONOMIC BEHAVIOR Jalota, D., Pavone, M., Qi, Q., Ye, Y. 2023; 141: 223-260
  • Control-oriented meta-learning INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Richards, S. M., Azizan, N., Slotine, J., Pavone, M. 2023
  • Trustworthy AI-Part II COMPUTER Mariani, R., Rossi, F., Cucchiara, R., Pavone, M., Simkin, B., Koene, A., Papenbrock, J. 2023; 56 (5): 13-16
  • Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms IEEE ROBOTICS AND AUTOMATION LETTERS Salzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., Ryll, M. 2023; 8 (4): 2397-2404
  • Co-design of communication and machine inference for cloud robotics AUTONOMOUS ROBOTS Nakanoya, M., Narasimhan, S., Bhat, S., Anemogiannis, A., Datta, A., Katti, S., Chinchali, S., Pavone, M. 2023
  • Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING Zardini, G., Lanzetti, N., Censi, A., Frazzoli, E., Pavone, M. 2023; 10 (2): 827-844
  • Online Routing Over Parallel Networks: Deterministic Limits and Data-driven Enhancements INFORMS JOURNAL ON COMPUTING Jalota, D., Paccagnan, D., Schiffer, M., Pavone, M. 2023
  • Trustworthy AI-Part 1 COMPUTER Mariani, R., Rossi, F., Cucchiara, R., Pavone, M., Simkin, B., Koene, A., Papenbrock, J. 2023; 56 (2): 14-18
  • Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots Alora, J., Cenedese, M., Schmerling, E., Haller, G., Pavone, M., IEEE IEEE. 2023: 2627-2633
  • Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems Wu, A., Lew, T., Solovey, K., Schmerling, E., Pavone, M., Asfour, T., Billard, A., Khatib, O. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 538-554
  • Sample-Efficient Safety Assurances Using Conformal Prediction Luo, R., Zhao, S., Kuck, J., Ivanovic, B., Savarese, S., Schmerling, E., Pavone, M., LaValle, S. M., O'Kane, J. M., Otte, M., Sadigh, D., Tokekar, P. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 149-169
  • Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications Banerjee, S., Sharma, A., Schmerling, E., Spolaor, M., Nemerouf, M., Pavone, M., IEEE IEEE. 2023
  • Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning Ivanovic, B., Harrison, J., Pavone, M., IEEE IEEE. 2023: 7786-7793
  • Tree-structured Policy Planning with Learned Behavior Models Chen, Y., Karkus, P., Ivanovic, B., Weng, X., Pavone, M., IEEE IEEE. 2023: 7902-7908
  • Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving Cosner, R. K., Chen, Y., Leung, K., Pavone, M., IEEE IEEE. 2023: 9757-9763
  • Learning Autonomous Vehicle Safety Concepts from Demonstrations Leung, K., Veer, S., Schmerling, E., Pavone, M., IEEE IEEE. 2023: 3193-3200
  • Interpretable Trajectory Prediction for Autonomous Vehicles via Counterfactual Responsibility Hsu, K., Leung, K., Chen, Y., Fisac, J. F., Pavone, M., IEEE IEEE. 2023: 5918-5925
  • Designing ReachBot: System Design Process with a Case Study of a Martian Lava Tube Mission Newdick, S., Chen, T. G., Hockman, B., Schmerling, E., Cutkosky, M. R., Pavone, M., IEEE IEEE. 2023
  • Motion Planning for a Climbing Robot with Stochastic Grasps Newdick, S., Ongole, N., Chen, T. G., Schmerling, E., Cutkosky, M. R., Pavone, M., IEEE IEEE. 2023: 11838-11844
  • FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization Yang, J., Pavone, M., Wang, Y., IEEE IEEE COMPUTER SOC. 2023: 8254-8263
  • Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles Veer, S., Leung, K., Cosner, R. K., Chen, Y., Karkus, P., Pavone, M., IEEE IEEE. 2023: 1507-1513
  • BITS: Bi-level Imitation for Traffic Simulation Xu, D., Chen, Y., Ivanovic, B., Pavone, M., IEEE IEEE. 2023: 2929-2936
  • Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models Christianos, F., Karkus, P., Ivanovic, B., Albrecht, S., Pavone, M., IEEE IEEE. 2023: 5558-5565
  • Guided Conditional Diffusion for Controllable Traffic Simulation Zhong, Z., Rempe, D., Xu, D., Chen, Y., Veer, S., Che, T., Ray, B., Pavone, M., IEEE IEEE. 2023: 3560-3566
  • Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk IEEE TRANSACTIONS ON AUTOMATIC CONTROL Chapman, M. P., Bonalli, R., Smith, K. M., Yang, I., Pavone, M., Tomlin, C. J. 2022; 67 (12): 6521-6536
  • Linear Reduced-Order Model Predictive Control IEEE TRANSACTIONS ON AUTOMATIC CONTROL Lorenzetti, J., McClellan, A., Farhat, C., Pavone, M. 2022; 67 (11): 5980-5995
  • SEQUENTIAL CONVEX PROGRAMMING FOR NON-LINEAR STOCHASTIC OPTIMAL CONTROL ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS Bonalli, R., Lew, T., Pavone, M. 2022; 28
  • Safe Reinforcement Learning Using Black-Box Reachability Analysis IEEE ROBOTICS AND AUTOMATION LETTERS Selim, M., Alanwar, A., Kousik, S., Gao, G., Pavone, M., Johansson, K. H. 2022; 7 (4): 10665-10672
  • < Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently IEEE CONTROL SYSTEMS MAGAZINE Malyuta, D., Reynolds, T. P., Szmuk, M., Lew, T., Bonalli, R., Pavone, M., Acikmese, B. 2022; 42 (5): 40-113
  • A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences McClellan, A., Lorenzetti, J., Pavone, M., Farhat, C. 2022; 380 (2229): 20210204

    Abstract

    This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance (DTI) for model predictive control (MPC); and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in realtime, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the DTI for MPC to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

    View details for DOI 10.1098/rsta.2021.0204

    View details for PubMedID 35719063

  • Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic. IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council Wollenstein-Betech, S., Salazar, M., Houshmand, A., Pavone, M., Paschalidis, I. C., Cassandras, C. G. 2022; 23 (8): 12263-12275

    Abstract

    This paper studies congestion-aware route-planning policies for intermodal Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility jointly with public transit under mixed traffic conditions (consisting of AMoD and private vehicles). First, we devise a network flow model to jointly optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture the effect of exogenous traffic stemming from private vehicles adapting to the AMoD flows in a user-centric fashion by leveraging a sequential approach. Since our results are in terms of link flows, we then provide algorithms to retrieve the explicit recommended routes to users. Finally, we showcase our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City, respectively. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows. However, blending AMoD with public transit, walking and micromobility options can significantly improve the overall system performance by leveraging the high-throughput of public transit combined with the flexibility of walking and micromobility.

    View details for DOI 10.1109/tits.2021.3112106

    View details for PubMedID 37124136

    View details for PubMedCentralID PMC10147341

  • Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Leung, K., Arechiga, N., Pavone, M. 2022
  • Testing Gecko-Inspired Adhesives with Astrobee Aboard the International Space Station: Readying the Technology for Space IEEE ROBOTICS & AUTOMATION MAGAZINE Chen, T. G., Cauligi, A., Suresh, S., Pavone, M., Cutkosky, M. 2022
  • Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework IEEE TRANSACTIONS ON ROBOTICS Lew, T., Sharma, A., Harrison, J., Bylard, A., Pavone, M. 2022
  • Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation. Surgical innovation Bourdillon, A. T., Garg, A., Wang, H., Woo, Y. J., Pavone, M., Boyd, J. 2022: 15533506221095298

    Abstract

    Background. The revolutions in AI hold tremendous capacity to augment human achievements in surgery, but robust integration of deep learning algorithms with high-fidelity surgical simulation remains a challenge. We present a novel application of reinforcement learning (RL) for automating surgical maneuvers in a graphical simulation.Methods. In the Unity3D game engine, the Machine Learning-Agents package was integrated with the NVIDIA FleX particle simulator for developing autonomously behaving RL-trained scissors. Proximal Policy Optimization (PPO) was used to reward movements and desired behavior such as movement along desired trajectory and optimized cutting maneuvers along the deformable tissue-like object. Constant and proportional reward functions were tested, and TensorFlow analytics was used to informed hyperparameter tuning and evaluate performance.Results. RL-trained scissors reliably manipulated the rendered tissue that was simulated with soft-tissue properties. A desirable trajectory of the autonomously behaving scissors was achieved along 1 axis. Proportional rewards performed better compared to constant rewards. Cumulative reward and PPO metrics did not consistently improve across RL-trained scissors in the setting for movement across 2 axes (horizontal and depth).Conclusion. Game engines hold promising potential for the design and implementation of RL-based solutions to simulated surgical subtasks. Task completion was sufficiently achieved in one-dimensional movement in simulations with and without tissue-rendering. Further work is needed to optimize network architecture and parameter tuning for increasing complexity.

    View details for DOI 10.1177/15533506221095298

    View details for PubMedID 35503302

  • Online Hypergraph Matching with Delays OPERATIONS RESEARCH Pavone, M., Saberi, A., Schiffer, M., Tsao, M. 2022
  • 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
  • Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics IEEE ROBOTICS AND AUTOMATION LETTERS Zhao, P., Lakshmanan, A., Ackerman, K., Gahlawat, A., Pavone, M., Hovakimyan, N. 2022; 7 (2): 5528-5535
  • Optimal Picking Policies in E-Commerce Warehouses MANAGEMENT SCIENCE Schiffer, M., Boysen, N., Klein, P. S., Laporte, G., Pavone, M. 2022
  • Trust but Verify: Cryptographic Data Privacy for Mobility Management IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Tsao, M., Yang, K., Zoepf, S., Pavone, M. 2022; 9 (1): 50-61
  • Second-Order Sensitivity Analysis for Bilevel Optimization Dyro, R., Schmerling, E., Arechiga, N., Pavone, M., Camps-Valls, G., Ruiz, F. J., Valera JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022
  • Motron: Multimodal Probabilistic Human Motion Forecasting Salzmann, T., Pavone, M., Ryll, M., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 6447-6456
  • Whose Track Is It Anyway? Improving Robustness to Tracking Errors with Affinity-based Trajectory Prediction Weng, X., Ivanovic, B., Kitani, K., Pavone, M., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 6563-6572
  • AdvDO: Realistic Adversarial Attacks for Trajectory Prediction Cao, Y., Xiao, C., Anandkumar, A., Xu, D., Pavone, M., Avidan, S., Brostow, G., Cisse, M., Farinella, G. M., Hassner, T. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 36-52
  • ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning Chen, Y., Ivanovic, B., Pavone, M., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 17082-17091
  • Private Location Sharing for Decentralized Routing Services Tsao, M., Yang, K., Gopalakrishnan, K., Pavone, M., IEEE IEEE. 2022: 2479-2486
  • Propagating State Uncertainty Through Trajectory Forecasting Ivanovic, B., Lin, Y., Shrivastava, S., Chakravarty, P., Pavone, M., IEEE IEEE. 2022: 2351-2358
  • Using Spectral Submanifolds for Nonlinear Periodic Control Mahlknecht, F., Alora, J., Jain, S., Schmerling, E., Bonalli, R., Haller, G., Pavone, M., IEEE IEEE. 2022: 6548-6555
  • Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications Leung, K., Pavone, M., IEEE IEEE. 2022: 178-185
  • Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty Sinha, R., Harrison, J., Richards, S. M., Pavone, M., IEEE IEEE. 2022: 906-913
  • Injecting Planning-Awareness into Prediction and Detection Evaluation Ivanovic, B., Pavone, M., IEEE IEEE. 2022: 821-828
  • MTP: Multi-hypothesis Tracking and Prediction for Reduced Error Propagation Weng, X., Ivanovic, B., Pavone, M., IEEE IEEE. 2022: 1218-1225
  • A Unified View of SDP-based Neural Network Verification through Completely Positive Programming Brown, R., Schmerling, E., Azizan, N., Pavone, M., Camps-Valls, G., Ruiz, F. J., Valera JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022
  • Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty Ivanovic, B., Lee, K., Tokmakov, P., Wulfe, B., McAllister, R., Gaidon, A., Pavone, M., IEEE IEEE. 2022: 12196-12203
  • ReachBot: A Small Robot with Exceptional Reach for Rough Terrain Chen, T. G., Miller, B., Winston, C., Schneider, S., Bylard, A., Pavone, M., Cutkosky, M. R., IEEE IEEE. 2022: 4517-4523
  • Bilevel Optimization for Planning Through Contact: A Semidirect Method Landry, B., Lorenzetti, J., Manchester, Z., Pavone, M., Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 789-804
  • Analysis and Control of Autonomous Mobility-on-Demand Systems ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS Zardini, G., Lanzetti, N., Pavone, M., Frazzoli, E. 2022; 5: 633-658
  • Control Barrier Functions for Cyber-Physical Systems and Applications to NMPC IEEE ROBOTICS AND AUTOMATION LETTERS Schilliger, J., Lew, T., Richards, S. M., Hanggi, S., Pavone, M., Onder, C. 2021; 6 (4): 8623-8630
  • Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Wollenstein-Betech, S., Salazar, M., Houshmand, A., Pavone, M., Paschalidis, I., Cassandras, C. G. 2021
  • Network offloading policies for cloud robotics: a learning-based approach AUTONOMOUS ROBOTS Chinchali, S., Sharma, A., Harrison, J., Elhafsi, A., Kang, D., Pergament, E., Cidon, E., Katti, S., Pavone, M. 2021
  • On Local Computation for Network-Structured Convex Optimization in Multiagent Systems IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Brown, R., Rossi, F., Solovey, K., Tsao, M., Wolf, M. T., Pavone, M. 2021; 8 (2): 542-554
  • Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach IEEE ROBOTICS AND AUTOMATION LETTERS Ivanovic, B., Leung, K., Schmerling, E., Pavone, M. 2021; 6 (2): 295–302
  • Vision-based Autonomous Disinfection of High-touch Surfaces in Indoor Environments Roelofs, S., Landry, B., Jalil, M., Martin, A., Koppaka, S., Tang, S. Y., Pavone, M., IEEE IEEE. 2021: 263-270
  • Real-Time Control of Mixed Fleets in Mobility-on-Demand Systems Yang, K., Tsao, M. W., Xu, X., Pavone, M., IEEE IEEE. 2021: 3570-3577
  • Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) Luke, J., Salazar, M., Rajagopal, R., Pavone, M. 2021: 3340-3347
  • Particle MPC for Uncertain and Learning-Based Control Dyro, R., Harrison, J., Sharma, A., Pavone, M., IEEE IEEE. 2021: 7127-7134
  • Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems Bylard, A., Bonalli, R., Pavone, M., IEEE IEEE. 2021: 7464-7470
  • Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions Schaefer, S., Leung, K., Ivanovic, B., Pavone, M., IEEE IEEE. 2021: 9673-9679
  • Near-Optimal Multi-Robot Motion Planning with Finite Sampling Dayan, D., Solovey, K., Pavone, M., Halperin, D., IEEE IEEE. 2021: 9190-9196
  • Soft Robot Optimal Control Via Reduced Order Finite Element Models Tonkens, S., Lorenzett, J., Pavone, M., IEEE IEEE. 2021: 12010-12016
  • Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition Solovey, K., Bandyopadhyay, S., Rossi, F., Wolf, M. T., Pavone, M., IEEE IEEE. 2021: 9117-9123
  • Efficient Large-Scale Multi-Drone Delivery using Transit Networks JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH Choudhury, S., Solovey, K., Kochenderfer, M. J., Pavone, M. 2021; 70: 757-788
  • Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems Richards, S. M., Azizan, N., Slotine, J., Pavone, M., Shell, D. A., Toussaint, M., Hsieh, M. A. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2021
  • Lyapunov-stable neural-network control Dai, H., Landry, B., Yang, L., Pavone, M., Tedrake, R., Shell, D. A., Toussaint, M., Hsieh, M. A. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2021
  • Co-Design of Communication and Machine Inference for Cloud Robotics Nakanoya, M., Chinchali, S., Anemogiannis, A., Datta, A., Katti, S., Pavone, M., Shell, D. A., Toussaint, M., Hsieh, M. A. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2021
  • On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks --- An Optimal Power Flow Approach IEEE Transactions on Control of Network Systems Estandia, A., Schiffer, M., Rossi, F., Luke, J., Kara, E. C., Rajagopal, R., Pavone, M. 2021
  • Soft Tensegrity Systems for Planetary Landing and Exploration Garanger, K., Krajewski, M., del Valle, I., Raheja, U., Rimoli, J. J., Rath, M., Pavone, M., Vansusante, P. J., Roberts, A. D. AMER SOC CIVIL ENGINEERS. 2021: 841-854
  • Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems Gammelli, D., Yang, K., Harrison, J., Rodrigues, F., Pereira, F. C., Pavone, M., IEEE IEEE. 2021: 2996-3003
  • Intermodal Autonomous Mobility-on-Demand IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Salazar, M., Lanzetti, N., Rossi, F., Schiffer, M., Pavone, M. 2020; 21 (9): 3946–60
  • Learning stabilizable nonlinear dynamics with contraction-based regularization INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Singh, S., Richards, S. M., Sindhwani, V., Slotine, J. E., Pavone, M. 2020
  • On infusing reachability-based safety assurance within planning frameworks for human-robot vehicle interactions INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Leung, K., Schmerling, E., Zhang, M., Chen, M., Talbot, J., Gerdes, J., Pavone, M. 2020
  • Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft JOURNAL OF GUIDANCE CONTROL AND DYNAMICS Mote, M., Egerstedt, M., Feron, E., Bylard, A., Pavone, M. 2020; 43 (7): 1247–58

    View details for DOI 10.2514/1.G004788

    View details for Web of Science ID 000542959700003

  • On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Rossi, F., Iglesias, R., Alizadeh, M., Pavone, M. 2020; 7 (1): 384–97
  • A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems Boewing, F., Schiffer, M., Salazar, M., Pavone, M., IEEE IEEE. 2020: 248–55
  • On Infusing Reachability-Based Safety Assurance Within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions Leung, K., Schmerling, E., Chen, M., Talbot, J., Gerdes, J., Pavone, M., Xiao, J., Kroger, T., Khatib, O. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 561-574
  • Sample Complexity of Probabilistic Roadmaps via c -nets Tsao, M., Solovey, K., Pavone, M., IEEE IEEE. 2020: 2196-2202
  • Map-Predictive Motion Planning in Unknown Environments Elhafsi, A., Ivanovic, B., Janson, L., Pavone, M., IEEE IEEE. 2020: 8552-8558
  • Revisiting the Asymptotic Optimality of RRT Solovey, K., Janson, L., Schmerling, E., Frazzoli, E., Pavone, M., IEEE IEEE. 2020: 2189-2195
  • Efficient Large-Scale Multi-Drone Delivery Using Transit Networks Choudhury, S., Solovey, K., Kochenderfer, M. J., Pavone, M., IEEE IEEE. 2020: 4543-4550
  • Shapeshifter: A Multi-Agent, Multi-Modal Robotic Platform for Exploration of Titan Tagliabue, A., Schneider, S., Pavone, M., Agha-mohammadi, A., IEEE IEEE. 2020
  • Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization Banerjee, S., Lew, T., Bonalli, R., Alfaadhel, A., Alomar, I., Shageer, H. M., Pavone, M., IEEE IEEE. 2020
  • Counter-example guided synthesis of neural network Lyapunov functions for piecewise linear systems Dai, H., Landry, B., Pavone, M., Tedrake, R., IEEE IEEE. 2020: 1274-1281
  • Error Bounds for Reduced Order Model Predictive Control Lorenzetti, J., Pavone, M., IEEE IEEE. 2020: 2521-2528
  • On the Co-Design of AV-Enabled Mobility Systems Zardini, G., Lanzetti, N., Salazar, M., Censi, A., Frazzoli, E., Pavone, M., IEEE IEEE. 2020
  • Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic Wollenstein-Betech, S., Houshmand, A., Salazar, M., Pavone, M., Cassandras, C. G., Paschalidis, I., IEEE IEEE. 2020
  • Interpretable Policies from Formally-Specified Temporal Properties DeCastro, J., Leung, K., Arechiga, N., Pavone, M., IEEE IEEE. 2020
  • Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions Wang, X., Leung, K., Pavone, M., IEEE IEEE. 2020: 6252-6259
  • Stochastic Motion Planning for Hopping Rovers on Small Solar System Bodies Hockman, B., Pavone, M., Amato, N. M., Hager, G., Thomas, S., TorresTorriti, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 877–93
  • How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics Majumdar, A., Pavone, M., Amato, N. M., Hager, G., Thomas, S., TorresTorriti, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 75–84
  • Multi-objective Optimal Control for Proactive Decision Making with Temporal Logic Models Chinchali, S. P., Livingston, S. C., Pavone, M., Amato, N. M., Hager, G., Thomas, S., TorresTorriti, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 127–44
  • Perception-Aware Motion Planning via Multiobjective Search on GPUs Ichter, B., Landry, B., Schmerling, E., Pavone, M., Amato, N. M., Hager, G., Thomas, S., TorresTorriti, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 895–912
  • Joint Design and Control of Electric Vehicle Propulsion Systems Verbruggen, F., Salazar, M., Pavone, M., Hofman, T., IEEE IEEE. 2020: 1725–31
  • Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization Lew, T., Bonalli, R., Pavone, M., IEEE IEEE. 2020: 1871–78
  • A Simple and Efficient Tube-based Robust Output Feedback Model Predictive Control Scheme Lorenzetti, J., Pavone, M., IEEE IEEE. 2020: 1775–82
  • Exploiting Locality and Structure for Distributed Optimization in Multi-Agent Systems Brown, R., Rossi, F., Solovey, K., Wolf, M. T., Pavone, M., IEEE IEEE. 2020: 440–47
  • 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
  • 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
  • ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Li Fei-Fei, Pavone, M., Amato, N. M., Hager, G., Thomas, S., TorresTorriti, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 437–53
  • Multi-objective optimal control for proactive decision making with temporal logic models INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Chinchali, S. P., Livingston, S. C., Chen, M., Pavone, M. 2019
  • 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
  • Backpropagation for Parametric STL Leung, K., Arechiga, N., Pavone, M., IEEE IEEE. 2019: 185–92
  • A Risk-Sensitive Finite-Time Reachability Approach for Safety of Stochastic Dynamic Systems Chapman, M. P., Lacotte, J., Tamar, A., Lee, D., Smith, K. M., Cheng, V., Fisac, J. F., Jha, S., Pavone, M., Tomlin, C. J., IEEE IEEE. 2019: 2958-2963
  • Optimal Routing and Energy Management Strategies for Plug-in Hybrid Electric Vehicles Salazar, M., Houshmand, A., Cassandras, C. G., Pavone, M., IEEE IEEE. 2019: 733–39
  • A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand Zgraggen, J., Tsao, M., Salazar, M., Schiffer, M., Pavone, M., IEEE IEEE. 2019: 1953–60
  • Perception-Constrained Robot Manipulator Planning for Satellite Servicing Zahroof, T., Bylard, A., Shageer, H., Pavone, M., IEEE IEEE. 2019
  • The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs Ivanovic, B., Pavone, M., IEEE IEEE COMPUTER SOC. 2019: 2375–84
  • Reduced Order Model Predictive Control For Setpoint Tracking Lorenzetti, J., Landry, B., Singh, S., Pavone, M., IEEE IEEE. 2019: 299–306
  • A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems Salazar, M., Tsao, M., Aguiar, I., Schiffer, M., Pavone, M., IEEE IEEE. 2019: 3040–46
  • Risk-Sensitive Generative Adversarial Imitation Learning Lacotte, J., Ghavamzadeh, M., Chow, Y., Pavone, M., Chaudhuri, K., Sugiyama, M. MICROTOME PUBLISHING. 2019
  • Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach Bonalli, R., Bylard, A., Cauligi, A., Lew, T., Pavone, M., Bicchi, A., KressGazit, H., Hutchinson, S. MIT PRESS. 2019
  • A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization Landry, B., Manchester, Z., Pavone, M., Bicchi, A., KressGazit, H., Hutchinson, S. MIT PRESS. 2019
  • Network Offloading Policies for Cloud Robotics: a Learning-based Approach Chinchali, S., Sharma, A., Harrison, J., Elhafsi, A., Kang, D., Pergament, E., Cidon, E., Katti, S., Pavone, M., Bicchi, A., KressGazit, H., Hutchinson, S. MIT PRESS. 2019
  • High-Dimensional Optimization in Adaptive Random Subspaces Lacotte, J., Pilanci, M., Pavone, M., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning Ivanovic, B., Harrison, J., Sharma, A., Chen, M., Pavone, M., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 15–21
  • Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems Tsao, M., Milojevic, D., Ruch, C., Salazar, M., Frazzoli, E., Pavone, M., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 6665–71
  • GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming Bonalli, R., Cauligi, A., Bylard, A., Pavone, M., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 6741–47
  • 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
  • Risk-Constrained Reinforcement Learning with Percentile Risk Criteria JOURNAL OF MACHINE LEARNING RESEARCH Chow, Y., Ghavamzadeh, M., Janson, L., Pavone, M. 2018; 18
  • On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms Rossi, F., Iglesias, R., Alizadeh, M., Pavone, M., KressGazit, H., Srinivasa, S., Howard, T., Atanasov, N. MIT PRESS. 2018
  • Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies Janson, L., Hu, T., Pavone, M., KressGazit, H., Srinivasa, S., Howard, T., Atanasov, N. MIT PRESS. 2018
  • Cellular Network Traffic Scheduling with Deep Reinforcement Learning Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., Katti, S., AAAI ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 766–74
  • 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
  • Learning Sampling Distributions for Robot Motion Planning Ichter, B., Harrison, J., Pavone, M., IEEE IEEE COMPUTER SOC. 2018: 7087–94
  • 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
  • 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
  • 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
  • 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
  • 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
  • Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction Schmerling, E., Leung, K., Vollprecht, W., Pavone, M., IEEE IEEE COMPUTER SOC. 2018: 3399–3406
  • 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

  • The Team Surviving Orienteers Problem: Routing Robots in Uncertain Environments with Survival Constraints Jorgensen, S., Chen, R. H., Milam, M. B., Pavone, M., IEEE IEEE. 2017: 227-234
  • Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs Ichter, B., Schmerling, E., Pavone, M., IEEE IEEE. 2017: 219-226
  • Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion Bylard, A., MacPherson, R., Hockman, B., Cutkosky, M. R., Pavone, M., IEEE IEEE. 2017
  • Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning Schmerling, E., Pavone, M., Amato, N., Srinivasa, S., Ayanian, N., Kuindersma, S. MIT PRESS. 2017
  • Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models Majumdar, A., Singh, S., Mandlekar, A., Pavone, M., Amato, N., Srinivasa, S., Ayanian, N., Kuindersma, S. MIT PRESS. 2017
  • Low Cost, High Endurance, Altitude-Controlled Latex Balloon for Near-Space Research (ValBal) Sushko, A., Tedjarati, A., Creus-Costa, J., Maldonado, S., Marshland, K., Pavone, M., IEEE IEEE. 2017
  • 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
  • Flying Smartphones: When Portable Computing Sprouts Wings IEEE PERVASIVE COMPUTING Allen, R., Pavone, M., Schwager, M. 2016; 15 (3): 83-88
  • 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
  • Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives Estrada, M. A., Hockman, B., Bylard, A., Hawkes, E. W., Cutkosky, M. R., Pavone, 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: 4907-4913
  • Model Predictive Control of Autonomous Mobility-on-Demand Systems Zhang, R., Rossi, F., Pavone, 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: 1382-1389
  • Simultaneous Model Identification and Task Satisfaction in the Presence of Temporal Logic Constraints Chinchali, S. P., Livingston, S. C., Pavone, M., Burdick, J. W., 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: 3682-3689
  • Spacecraft Autonomy Challenges for Next-Generation Space Missions Starek, J. A., Acikmese, B., Nesnas, I. A., Pavone, M., Feron, E. SPRINGER-VERLAG BERLIN. 2016: 1-48
  • Fast Marching Trees: A Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions Janson, L., Pavone, M., Inaba, M., Corke, P. SPRINGER-VERLAG BERLIN. 2016: 667-684
  • Real-Time, Propellant-Optimized Spacecraft Motion Planning under Clohessy-Wiltshire-Hill Dynamics Starek, J. A., Schmerling, E., Maher, G. D., Barbee, B. W., Pavone, M., IEEE IEEE. 2016
  • Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms Zhang, R., Rossi, F., Pavone, M., Hsu, D., Amato, N., Berman, S., Jacobs, S. MIT PRESS. 2016
  • Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk Carpin, S., Chow, Y., Pavone, 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: 335-342
  • Autonomous Calibration of MEMS Disk Resonating Gyroscope for Improved Sensor Performance Flader, I. B., Ahn, C. H., Gerrard, D. D., Ng, E. J., Yang, Y., Hong, V. A., Pavone, M., Kenny, T. W., IEEE IEEE. 2016: 5803–10
  • 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
  • Guest Editorial: Special issue on constrained decision-making in robotics AUTONOMOUS ROBOTS Pavone, M., Carpin, S. 2015; 39 (4): 465-467
  • 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

    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 DOI 10.1177/0278364915577958

    View details for Web of Science ID 000355612400004

    View details for PubMedCentralID PMC4798023

  • 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 DOI 10.1177/0278364915577958

    View details for PubMedID 27003958

    View details for PubMedCentralID PMC4798023

  • 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

  • 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. n., Janson, L. n., Pavone, M. n. 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

  • Toward a Real-Time Framework for Solving the Kinodynamic Motion Planning Problem Allen, R., Pavone, M., IEEE IEEE COMPUTER SOC. 2015: 928-934
  • Decentralized Algorithms for 3D Symmetric Formations in Robotic Networks - a Contraction Theory Approach Singh, S., Schmerling, E., Pavone, M., IEEE IEEE COMPUTER SOC. 2015: 1274-1281
  • A Queueing Network Approach to the Analysis and Control of Mobility-On-Demand Systems Zhang, R., Pavone, M., IEEE IEEE. 2015: 4702-4709
  • Models, Algorithms, and Evaluation for Autonomous Mobility-On-Demand Systems Zhang, R., Spieser, K., Frazzoli, E., Pavone, M., IEEE IEEE. 2015: 2573-2587
  • A SAMPLING-BASED APPROACH TO SPACECRAFT AUTONOMOUS MANEUVERING WITH SAFETY SPECIFICATIONS Starek, J. A., Barbee, B., Pavone, M., Gravseth, I. J. UNIVELT INC. 2015: 725-737
  • A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots Zhu, Z., Schmerling, E., Pavone, M., IEEE IEEE. 2015: 835-842
  • Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Chow, Y., Tamar, A., Mannor, S., Pavone, M., Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
  • A Unifying Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms Chow, Y., L., Pavone, M. 2014
  • A Dynamical Characterization of Internally-Actuated Microgravity Mobility Systems Koenig, A. W., Pavone, M., Castillo-Rogez, J. C., Nesnas, I. D., IEEE IEEE. 2014: 6618-6624
  • Rapid Multirobot Deployment with Time Constraints Carpin, S., Pavone, M., Sadler, B. M., IEEE IEEE. 2014: 1147-1154
  • Distributed consensus with mixed time/communication bandwidth performance metrics Rossi, F., Pavone, M., IEEE IEEE. 2014: 286-293
  • On the Fundamental Limitations of Performance for Distributed Decision-Making in Robotic Networks Rossi, F., Pavone, M., IEEE IEEE. 2014: 2433-2440
  • 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
  • A Uniform-Grid Discretization Algorithm for Stochastic Optimal Control with Risk Constraints Chow, Y., Pavone, M., IEEE IEEE. 2013: 2470-2475
  • 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
  • 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. n., Janson, L. n., Moreno, L. n., Pavone, M. n. ; 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