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.
Code as Policies: Language Model Programs for Embodied Control
2023295 citationsJacky Liang, Wenlong Huang et al.profile →
iNeRF: Inverting Neural Radiance Fields for Pose Estimation
2021217 citationsYen-Chen Lin, Pete Florence et al.2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)profile →
Interactive Language: Talking to Robots in Real Time
202446 citationsCorey Lynch, Ayzaan Wahid et al.IEEE Robotics and Automation Lettersprofile →
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 Pete Florence'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 Pete Florence with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pete Florence more than expected).
This network shows the impact of papers produced by Pete Florence. 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 Pete Florence. The network helps show where Pete Florence may publish in the future.
Co-authorship network of co-authors of Pete Florence
This figure shows the co-authorship network connecting the top 25 collaborators of Pete Florence.
A scholar is included among the top collaborators of Pete Florence 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 Pete Florence. Pete Florence is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
17 of 17 papers shown
1.
Lynch, Corey, et al.. (2024). Interactive Language: Talking to Robots in Real Time. IEEE Robotics and Automation Letters. 1–8.46 indexed citations breakdown →
2.
Liang, Jacky, Wenlong Huang, Fei Xia, et al.. (2023). Code as Policies: Language Model Programs for Embodied Control. 9493–9500.295 indexed citations breakdown →
Lin, Yen-Chen, Pete Florence, Jonathan T. Barron, et al.. (2021). iNeRF: Inverting Neural Radiance Fields for Pose Estimation. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1323–1330.217 indexed citations breakdown →
12.
Manuelli, Lucas, Yunzhu Li, Pete Florence, & Russ Tedrake. (2020). Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning. 693–710.6 indexed citations
13.
Florence, Pete, Lucas Manuelli, & Russ Tedrake. (2018). Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. 373–385.35 indexed citations
Marion, Pat, Pete Florence, Lucas Manuelli, & Russ Tedrake. (2017). A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes.. arXiv (Cornell University).2 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.