Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
2023272 citationsIshika Singh, Valts Blukis et al.profile →
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
202389 citationsDavid Hoeller, B. N. Babich et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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).
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.
Liu, Bo, Qiang Liu, Peter Stone, et al.. (2021). Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. CaltechAUTHORS (California Institute of Technology). 6860–6870.4 indexed citations
8.
Bharadhwaj, Homanga, Aviral Kumar, Nicholas Rhinehart, et al.. (2021). Conservative Safety Critics for Exploration. International Conference on Learning Representations.4 indexed citations
9.
Caterini, Anthony L., et al.. (2021). C-Learning: Horizon-Aware Cumulative Accessibility Estimation. International Conference on Learning Representations.1 indexed citations
Nie, Weili, Tero Karras, Animesh Garg, et al.. (2020). Semi-Supervised StyleGAN for Disentanglement Learning. CaltechAUTHORS (California Institute of Technology). 1. 7360–7369.18 indexed citations
13.
Da, Xingye, Zhaoming Xie, David Hoeller, et al.. (2020). Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. arXiv (Cornell University). 883–894.6 indexed citations
14.
Li, Yunzhu, Antonio Torralba, Animashree Anandkumar, Dieter Fox, & Animesh Garg. (2020). Causal Discovery in Physical Systems from Videos. CaltechAUTHORS (California Institute of Technology). 33. 9180–9192.2 indexed citations
15.
Fang, Kuan, Yuke Zhu, Animesh Garg, Silvio Savarese, & Li Fei-Fei. (2019). Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation.. 42–52.5 indexed citations
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.