Animesh Garg

70 papers receiving 2.2k citations

Hit Papers

ProgPrompt: Generating Situated Robot Task Plans using La...20232026202420252023202350100150200250

Peers

Animesh Garg
Comparison fields: 5 of 123
  • Computer Vision and Pattern Recognition 883
  • Artificial Intelligence 752
  • Control and Systems Engineering 728
  • Biomedical Engineering 702
  • Surgery 345
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Countries citing papers authored by Animesh Garg

Since Specialization
Citations

This map shows the geographic impact of Animesh Garg's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Animesh Garg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Animesh Garg more than expected).

Fields of papers citing papers by Animesh Garg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Animesh Garg. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Animesh Garg. The network helps show where Animesh Garg may publish in the future.

Co-authorship network of co-authors of Animesh Garg

This figure shows the co-authorship network connecting the top 25 collaborators of Animesh Garg. A scholar is included among the top collaborators of Animesh Garg based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Animesh Garg. Animesh Garg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 45
2 12
3 36
4 14
5 4
6 27
7 49
8 47
9
Conservative Safety Critics for Exploration
4
10
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
1
11
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition
4
12 21
13
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
6
14
Causal Discovery in Physical Systems from Videos
2
15
Angular Visual Hardness
3
16
Semi-Supervised StyleGAN for Disentanglement Learning
18
17
Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation.
5
18
Weakly Supervised Generative Adversarial Networks for 3D Reconstruction.
10
19 99
20 61

About Animesh Garg

Animesh Garg is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 70 papers that have together received 2.2k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (20 papers), Reinforcement Learning in Robotics (15 papers) and Surgical Simulation and Training (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (883 citations), Control and Systems Engineering (728 citations) and Health Informatics (33 citations). Animesh Garg has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Silvio Savarese, Ken Goldberg, Li Fei-Fei, Yuke Zhu, Danfei Xu, Siddarth Sen, Sanjay Krishnan, Dieter Fox, Pieter Abbeel and Ishika Singh. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The International Journal of Robotics Research and Medical Physics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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