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D-Index & Metrics

Computer Science

D-Index
65
Citations
16180
World Ranking
2466
National Ranking
1236

Research.com Recognitions

  • 2012 - Hellman Fellow

Overview

Marco Pavone is affiliated with Stanford University in the United States and has an extensive research portfolio in engineering and computer science. Their publications focus on various subfields including artificial intelligence, control and systems engineering, automotive engineering, computer vision and pattern recognition, and computational theory and mathematics.

The scientist's work covers several main topics of study that include autonomous vehicle technology and safety, anomaly detection techniques and applications, transportation and mobility innovations, robotic path planning algorithms, advanced control systems optimization, adversarial robustness in machine learning, and traffic control and management.

Recent publications authored or co-authored by Marco Pavone include:

  • Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently (2022), published in IEEE Control Systems
  • Text2Motion: from natural language instructions to feasible plans (2023), published in Autonomous Robots
  • Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms (2023), published in IEEE Robotics and Automation Letters
  • On infusing reachability-based safety assurance within planning frameworks for human-robot vehicle interactions (2020), published in The International Journal of Robotics Research
  • ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning (2022), published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Marco Pavone frequently collaborates with several researchers, including Boris Ivanovic, Edward Schmerling, Thomas Lew, Yuxiao Chen, and Devansh Jalota. The number of coauthored works ranges from 19 to 68 publications per collaborator.

Publication venues most commonly featuring their work are:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • The International Journal of Robotics Research
  • IEEE Transactions on Control of Network Systems
  • SSRN Electronic Journal

Marco Pavone received the Hellman Fellowship award in 2012.

Best Publications

  • Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data.

    Tim Salzmann;Boris Ivanovic;Punarjay Chakravarty;Marco Pavone

  • Fast marching tree

    Lucas Janson;Edward Schmerling;Ashley Clark;Marco Pavone

  • Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore

    Kevin Spieser;Kyle Ballantyne Treleaven;Rick Zhang;Emilio Frazzoli

  • Control of robotic mobility-on-demand systems

    Rick Zhang;Marco Pavone

  • The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

    Boris Ivanovic;Marco Pavone

  • Robotic load balancing for mobility-on-demand systems

    Marco Pavone;Stephen L Smith;Emilio Frazzoli;Daniela Rus

  • Learning Sampling Distributions for Robot Motion Planning

    Brian Ichter;James Harrison;Marco Pavone

  • Dynamic Vehicle Routing for Robotic Systems

    F. Bullo;E. Frazzoli;M. Pavone;K. Savla

  • FreeNeRF: Improving Few-Shot Neural Rendering with Free Frequency Regularization

    Unknown

  • Text2Motion: from natural language instructions to feasible plans

    Unknown

  • Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

    Yinlam Chow;Mohammad Ghavamzadeh;Lucas Janson;Marco Pavone

  • Cellular Network Traffic Scheduling With Deep Reinforcement Learning

    Sandeep Chinchali;Pan Hu;Tianshu Chu;Manu Sharma

  • Robust online motion planning via contraction theory and convex optimization

    Sumeet Singh;Anirudha Majumdar;Jean-Jacques Slotine;Marco Pavone

  • Decentralized Policies for Geometric Pattern Formation and Path Coverage

    Marco Pavone;Emilio Frazzoli

  • Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction

    Edward Schmerling;Karen Leung;Wolf Vollprecht;Marco Pavone

  • Robot Motion Planning in Learned Latent Spaces

    Brian Ichter;Marco Pavone

  • Risk-sensitive and robust decision-making: a CVaR optimization approach

    Yinlam Chow;Aviv Tamar;Shie Mannor;Marco Pavone

  • Model predictive control of autonomous mobility-on-demand systems

    Rick Zhang;Federico Rossi;Marco Pavone

  • Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment

    M Pavone;E Frazzoli;F Bullo

  • Distributed Control of Spacecraft Formations via Cyclic Pursuit: Theory and Experiments

    Jaime L. Ramirez-Riberos;Marco Pavone;Emilio Frazzoli;David W. Miller

  • Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms

    Federico Rossi;Rick Zhang;Yousef Hindy;Marco Pavone

  • Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems

    Ramon Iglesias;Federico Rossi;Kevin Wang;David Hallac

  • Fast Marching Trees: A Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions.

    Lucas Janson;Marco Pavone

  • Dynamic Vehicle Routing for Robotic Systems Planning optimal routes for multiple vehicles performing different tasks is discussed; fundamental limits on achievable performance are established for tasks that are generated by exogenous processes.

    Francesco Bullo;Emilio Frazzoli;Marco Pavone;Ketan Savla

Frequent Co-Authors

Francesco Bullo
Francesco Bullo University of California, Santa Barbara
Stephen L. Smith
Stephen L. Smith University of Guelph
Mac Schwager
Mac Schwager Stanford University
Claire J. Tomlin
Claire J. Tomlin University of California, Berkeley
Paolo Arena
Paolo Arena University of Catania
Mykel J. Kochenderfer
Mykel J. Kochenderfer Stanford University
Luigi Fortuna
Luigi Fortuna University of Catania
Julie Castillo-Rogez
Julie Castillo-Rogez California Institute of Technology

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