World's Best Scientists 2026 revealed!
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Engineering and Technology
USA
2025

D-Index & Metrics

Computer Science

D-Index
96
Citations
37493
World Ranking
437
National Ranking
242

Research.com Recognitions

  • 2025 - Research.com Engineering and Technology in United States Leader Award

Overview

Peter Stone is affiliated with The University of Texas at Austin in the United States. Their research contributions primarily focus on the intersection of computer science, artificial intelligence, and robotics.

The main fields of study include:

  • Computer Science

Their research delves into significant subfields of computer science, such as:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Aerospace Engineering
  • Social Psychology

Key topics addressed in their work feature:

  • Reinforcement Learning in Robotics
  • Robotic Path Planning Algorithms
  • Robot Manipulation and Learning
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Robotics and Sensor-Based Localization
  • Social Robot Interaction and HRI

Among their recent papers are the following notable publications:

  • "Outracing champion Gran Turismo drivers with deep reinforcement learning," 2022, Nature
  • "Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey," 2020, arXiv (Cornell University)
  • "Motion planning and control for mobile robot navigation using machine learning: a survey," 2022, Autonomous Robots
  • "A Lifelong Learning Approach to Mobile Robot Navigation," 2021, IEEE Robotics and Automation Letters
  • "Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles," 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Frequent co-authors collaborating with Peter Stone include:

  • Xuesu Xiao
  • Garrett Warnell
  • Haresh Karnan
  • Joydeep Biswas
  • Zizhao Wang

Their research is regularly published in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Robotics and Automation Letters
  • Autonomous Robots
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Best Publications

  • Transfer Learning for Reinforcement Learning Domains: A Survey

    Matthew E. Taylor;Peter Stone

  • Multiagent Systems: A Survey from a Machine Learning Perspective

    Peter Stone;Manuela Veloso

  • Efficacy of climate forcings

    J. Hansen;J. Hansen;M. Sato;R. Ruedy;L. Nazarenko

  • Climate Sensitivity: Analysis of Feedback Mechanisms

    J. Hansen;A. Lacis;D. Rind;G. Russell

  • A multiagent approach to autonomous intersection management

    Kurt Dresner;Peter Stone

  • Efficient Three-Dimensional Global Models for Climate Studies: Models I and II

    J. Hansen;G. Russell;D. Rind;P. Stone

  • Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model

    J. Hansen;I. Fung;A. Lacis;D. Rind

  • Present-Day Atmospheric Simulations Using GISS ModelE: Comparison to In Situ, Satellite, and Reanalysis Data

    Gavin A. Schmidt;Reto Ruedy;James E. Hansen;Igor Aleinov

  • Policy gradient reinforcement learning for fast quadrupedal locomotion

    N. Kohl;P. Stone

  • Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer

    Peter Stone

  • Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models

    M. Claussen;LA Mysak;AJ Weaver;Michel Crucifix

  • Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism

    Kurt Dresner;Peter Stone

  • Layered Learning

    Peter Stone;Manuela M. Veloso

  • Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork

    Peter Stone;Manuela Veloso

  • Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations

    Chris E. Forest;Peter H. Stone;Andrei P. Sokolov;Myles R. Allen

  • The RoboCup Synthetic Agent Challenge 97

    Hiroaki Kitano;Milind Tambe;Peter Stone;Manuela M. Veloso

  • Multi-gas assessment of the Kyoto Protocol

    John M. Reilly;Ronald G. Prinn;Jochen. Harnisch;Jean. Fitzmaurice

  • Outracing champion Gran Turismo drivers with deep reinforcement learning

    Unknown

  • Interactively shaping agents via human reinforcement: the TAMER framework

    W. Bradley Knox;Peter Stone

  • A Simplified Radiative-Dynamical Model for the Static Stability of Rotating Atmospheres

    Peter H. Stone

  • On Non-Geostrophic Baroclinic Stability

    Peter H. Stone

  • Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

    Stefano V. Albrecht;Peter Stone

  • Climate forcings in Goddard Institute for Space Studies SI2000 simulations

    J. Hansen;M. Sato;M. Sato;L. Nazarenko;L. Nazarenko;R. Ruedy

  • Multiagent traffic management: an improved intersection control mechanism

    Kurt Dresner;Peter Stone

  • Deep Recurrent Q-Learning for Partially Observable MDPs

    Matthew Hausknecht;Peter Stone

  • Ad hoc autonomous agent teams: collaboration without pre-coordination

    Peter Stone;Gal A. Kaminka;Sarit Kraus;Jeffrey S. Rosenschein

  • Evolutionary Function Approximation for Reinforcement Learning

    Shimon Whiteson;Peter Stone

  • Layered learning in multiagent systems

    Peter Stone

  • PAC Subset Selection in Stochastic Multi-armed Bandits

    Shivaram Kalyanakrishnan;Ambuj Tewari;Peter Auer;Peter Stone

  • Auction-based autonomous intersection management

    Dustin Carlino;Stephen D. Boyles;Peter Stone

  • Transfer Learning via Inter-Task Mappings for Temporal Difference Learning

    Matthew E. Taylor;Peter Stone;Yaxin Liu

Frequent Co-Authors

Tucker Balch
Tucker Balch Emory University
Masahiro Fujita
Masahiro Fujita Sony (Japan)
Michael D. Mauk
Michael D. Mauk The University of Texas at Austin
Sriram Vishwanath
Sriram Vishwanath The University of Texas at Austin
Satoshi Tadokoro
Satoshi Tadokoro Tohoku University
Bradley C. Love
Bradley C. Love University College London

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