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Computer Science

D-Index
35
Citations
7173
World Ranking
11516
National Ranking
4731

Overview

Jeannette Bohg is affiliated with Stanford University in the United States. Their research work primarily spans the fields of Computer Science and Engineering, with significant contributions to subfields such as Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, and Cognitive Neuroscience.

The main topics Jeannette Bohg focuses on in their research include:

  • Robot Manipulation and Learning
  • Reinforcement Learning in Robotics
  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Tactile and Sensory Interactions
  • Robotic Path Planning Algorithms
  • Domain Adaptation and Few-Shot Learning

Jeannette Bohg has published extensively, with frequent appearances in venues such as:

  • arXiv (Cornell University)
  • IEEE Robotics and Automation Letters
  • Autonomous Robots
  • IEEE Transactions on Robotics
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Some of their recent papers are:

  • On the Opportunities and Risks of Foundation Models, 2021, arXiv (Cornell University)
  • Deep Learning Approaches to Grasp Synthesis: A Review, 2023, IEEE Transactions on Robotics
  • Text2Motion: from natural language instructions to feasible plans, 2023, Autonomous Robots
  • TidyBot: personalized robot assistance with large language models, 2023, Autonomous Robots
  • Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects, 2020, IEEE Robotics and Automation Letters

Frequent coauthors collaborating with Jeannette Bohg include:

  • Dorsa Sadigh
  • Rika Antonova
  • Toki Migimatsu
  • Priya Sundaresan
  • Christopher Agia

Best Publications

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Data-Driven Grasp Synthesis—A Survey

    Jeannette Bohg;Antonio Morales;Tamim Asfour;Danica Kragic

  • Interactive Perception: Leveraging Action in Perception and Perception in Action

    Jeannette Bohg;Karol Hausman;Bharath Sankaran;Oliver Brock

  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Krishnan Srinivasan;Parth Shah

  • Leveraging big data for grasp planning

    Daniel Kappler;Jeannette Bohg;Stefan Schaal

  • Text2Motion: from natural language instructions to feasible plans

    Unknown

  • TidyBot: Personalized Robot Assistance with Large Language Models

    Unknown

  • MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences

    Xingyu Liu;Mengyuan Yan;Jeannette Bohg

  • Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

    Michelle A. Lee;Yuke Zhu;Peter Zachares;Matthew Tan

  • Learning grasping points with shape context

    Jeannette Bohg;Danica Kragic

  • Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects

    Mengyuan Yan;Yilin Zhu;Ning Jin;Jeannette Bohg

  • OpenGRASP: a toolkit for robot grasping simulation

    Beatriz León;Stefan Ulbrich;Rosen Diankov;Gustavo Puche

  • Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

    Roberto Martin-Martin;Michelle A. Lee;Rachel Gardner;Silvio Savarese

  • Learning of grasp selection based on shape-templates

    Alexander Herzog;Peter Pastor;Mrinal Kalakrishnan;Ludovic Righetti

  • Automatic LQR tuning based on Gaussian process global optimization

    Alonso Marco;Philipp Hennig;Jeannette Bohg;Stefan Schaal

  • Mind the gap - robotic grasping under incomplete observation

    Jeannette Bohg;Matthew Johnson-Roberson;Beatriz Leon;Javier Felip

  • Concept2Robot: Learning manipulation concepts from instructions and human demonstrations:

    Lin Shao;Toki Migimatsu;Qiang Zhang;Karen Yang

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models

    Unknown

  • Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

    Hsu-Kuang Chiu;Jie Li;Rares Ambrus;Jeannette Bohg

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

    Unknown

  • Real-Time Perception Meets Reactive Motion Generation

    Daniel Kappler;Franziska Meier;Jan Issac;Jim Mainprice

  • ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

    Unknown

  • Probabilistic object tracking using a range camera

    Manuel Wuthrich;Peter Pastor;Mrinal Kalakrishnan;Jeannette Bohg

  • DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

    Unknown

  • DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

    Unknown

  • Probabilistic Articulated Real-Time Tracking for Robot Manipulation

    Cristina Garcia Cifuentes;Jan Issac;Manuel Wüthrich;Stefan Schaal

  • Combining learned and analytical models for predicting action effects.

    Alina Kloss;Stefan Schaal;Jeannette Bohg

  • UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

    Lin Shao;Fabio Ferreira;Mikael Jorda;Varun Nambiar

  • Probabilistic 3D Multi-Object Tracking for Autonomous Driving.

    Hsu-Kuang Chiu;Antonio Prioletti;Jie Li;Jeannette Bohg

Frequent Co-Authors

Stefan Schaal
Stefan Schaal Google (United States)
Danica Kragic
Danica Kragic Royal Institute of Technology
Dorsa Sadigh
Dorsa Sadigh Stanford University
Silvio Savarese
Silvio Savarese Stanford University
Animesh Garg
Animesh Garg University of Toronto
Tamim Asfour
Tamim Asfour Karlsruhe Institute of Technology
Yuke Zhu
Yuke Zhu The University of Texas at Austin
Ludovic Righetti
Ludovic Righetti New York University
Li Fei-Fei
Li Fei-Fei Stanford University
Marc Toussaint
Marc Toussaint Technical University of Berlin

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