2026 Jeannette Bohg: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	35	11514	11184	4731	4527	174	7173

Jeannette Bohg publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jeannette Bohg sits on this spectrum.

32 publications 991+

This scientist: 174 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jeannette Bohg D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jeannette Bohg sits on this spectrum.

30 D-Index 131+

This scientist: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

2127
Citations
Data-Driven Grasp Synthesis—A Survey

Jeannette Bohg;Antonio Morales;Tamim Asfour;Danica Kragic

1163
Citations
Interactive Perception: Leveraging Action in Perception and Perception in Action

Jeannette Bohg;Karol Hausman;Bharath Sankaran;Oliver Brock

406
Citations
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

311
Citations
Leveraging big data for grasp planning

Daniel Kappler;Jeannette Bohg;Stefan Schaal

303
Citations
Text2Motion: from natural language instructions to feasible plans

Unknown

259
Citations
TidyBot: Personalized Robot Assistance with Large Language Models

Unknown

248
Citations
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences

Xingyu Liu;Mengyuan Yan;Jeannette Bohg

222
Citations
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

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

180
Citations
Learning grasping points with shape context

Jeannette Bohg;Danica Kragic

171
Citations
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects

Mengyuan Yan;Yilin Zhu;Ning Jin;Jeannette Bohg

162
Citations
OpenGRASP: a toolkit for robot grasping simulation

Beatriz León;Stefan Ulbrich;Rosen Diankov;Gustavo Puche

157
Citations
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

149
Citations
Learning of grasp selection based on shape-templates

Alexander Herzog;Peter Pastor;Mrinal Kalakrishnan;Ludovic Righetti

146
Citations
Automatic LQR tuning based on Gaussian process global optimization

Alonso Marco;Philipp Hennig;Jeannette Bohg;Stefan Schaal

137
Citations
Mind the gap - robotic grasping under incomplete observation

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

130
Citations
Concept2Robot: Learning manipulation concepts from instructions and human demonstrations:

Lin Shao;Toki Migimatsu;Qiang Zhang;Karen Yang

129
Citations
Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Unknown

120
Citations
Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving

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

115
Citations
Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

Unknown

115
Citations
Real-Time Perception Meets Reactive Motion Generation

Daniel Kappler;Franziska Meier;Jan Issac;Jim Mainprice

111
Citations
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

Unknown

96
Citations
Probabilistic object tracking using a range camera

Manuel Wuthrich;Peter Pastor;Mrinal Kalakrishnan;Jeannette Bohg

94
Citations
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Unknown

92
Citations
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Unknown

90
Citations
Probabilistic Articulated Real-Time Tracking for Robot Manipulation

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

86
Citations
Combining learned and analytical models for predicting action effects.

Alina Kloss;Stefan Schaal;Jeannette Bohg

84
Citations
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

Lin Shao;Fabio Ferreira;Mikael Jorda;Varun Nambiar

77
Citations
Probabilistic 3D Multi-Object Tracking for Autonomous Driving.

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

68
Citations

Frequent Co-Authors

Stefan Schaal Google (United States)

Danica Kragic Royal Institute of Technology

Lin Shao Texas A&M University

Dorsa Sadigh Stanford University

Silvio Savarese Stanford University

Animesh Garg University of Toronto

Tamim Asfour Karlsruhe Institute of Technology

Yuke Zhu The University of Texas at Austin

Ludovic Righetti New York University

Li Fei-Fei Stanford University

External Links

Personal website of Jeannette Bohg

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