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Sertac Karaman

Sertac Karaman

D-Index & Metrics

Computer Science

D-Index
60
Citations
20055
World Ranking
3188
National Ranking
1545

Overview

Sertac Karaman is affiliated with MIT in the United States and has published extensively in the fields of Computer Science and Engineering. Their research spans major subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Automotive Engineering, and Control and Systems Engineering.

The scientist's work focuses on several core topics within robotics and autonomous systems. These include Autonomous Vehicle Technology and Safety, Reinforcement Learning in Robotics, Robotic Path Planning Algorithms, Robotics and Sensor-Based Localization, Advanced Vision and Imaging, Guidance and Control Systems, and Advanced Neural Network Applications.

Some of their recent notable papers include:

  • Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation, 2020, IEEE Robotics and Automation Letters
  • VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles, 2022, 2022 International Conference on Robotics and Automation (ICRA)
  • Improving Age of Information in Wireless Networks With Perfect Channel State Information, 2020, IEEE/ACM Transactions on Networking
  • Aerobatic Trajectory Generation for a VTOL Fixed-Wing Aircraft Using Differential Flatness, 2023, IEEE Transactions on Robotics
  • Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features, 2021, IEEE Robotics and Automation Letters

Frequent collaborators include Daniela Rus, Igor Gilitschenski, Ezra Tal, Vivienne Sze, and Wilko Schwarting.

Their publications are commonly found in venues such as arXiv (Cornell University), IEEE Robotics and Automation Letters, The International Journal of Robotics Research, 2022 International Conference on Robotics and Automation (ICRA), and IEEE Transactions on Robotics.

Best Publications

  • Sampling-based algorithms for optimal motion planning

    Sertac Karaman;Emilio Frazzoli

  • Real-Time Motion Planning With Applications to Autonomous Urban Driving

    Y. Kuwata;S. Karaman;J. Teo;E. Frazzoli

  • Anytime Motion Planning using the RRT

    Sertac Karaman;Matthew R. Walter;Alejandro Perez;Emilio Frazzoli

  • Incremental Sampling-based Algorithms for Optimal Motion Planning.

    Sertac Karaman;Emilio Frazzoli

  • Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

    Fangchang Mal;Sertac Karaman

  • A perception-driven autonomous urban vehicle

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Optimal kinodynamic motion planning using incremental sampling-based methods

    Sertac Karaman;Emilio Frazzoli

  • Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera

    Fangchang Ma;Guilherme Venturelli Cavalheiro;Sertac Karaman

  • Social behavior for autonomous vehicles

    Wilko Schwarting;Alyssa Pierson;Javier Alonso-Mora;Sertac Karaman

  • A Perception Driven Autonomous Urban Robot

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • FastDepth: Fast Monocular Depth Estimation on Embedded Systems

    Diana Wofk;Fangchang Ma;Tien-Ju Yang;Sertac Karaman

  • Accurate Tracking of Aggressive Quadrotor Trajectories Using Incremental Nonlinear Dynamic Inversion and Differential Flatness

    Ezra Tal;Sertac Karaman

  • Optimizing Information Freshness in Wireless Networks Under General Interference Constraints

    Rajat Talak;Sertac Karaman;Eytan Modiano

  • Duckietown: An open, inexpensive and flexible platform for autonomy education and research

    Liam Paull;Jacopo Tani;Heejin Ahn;Javier Alonso-Mora

  • Minimizing age-of-information in multi-hop wireless networks

    Rajat Talak;Sertac Karaman;Eytan Modiano

  • Sampling-based motion planning with deterministic μ-calculus specifications

    Sertac Karaman;Emilio Frazzoli

  • Optimal control of Mixed Logical Dynamical systems with Linear Temporal Logic specifications

    S. Karaman;R.G. Sanfelice;E. Frazzoli

  • Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

    Alexander Amini;Igor Gilitschenski;Jacob Phillips;Julia Moseyko

  • Motion Planning in Complex Environments using Closed-loop Prediction

    Unknown

  • FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality

    Winter Guerra;Ezra Tal;Varun Murali;Gilhyun Ryou

  • Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

    Jeong Hwan Jeon;Raghvendra V. Cowlagi;Steven C. Peters;Sertac Karaman

  • Sampling-based optimal motion planning for non-holonomic dynamical systems

    Sertac Karaman;Emilio Frazzoli

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