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.
CHOMP: Gradient optimization techniques for efficient motion planning
2009650 citationsNathan Ratliff, Matt Zucker et al.Figshareprofile →
CHOMP: Covariant Hamiltonian optimization for motion planning
2013470 citationsMatt Zucker, Nathan Ratliff et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Nathan Ratliff
Since
Specialization
Citations
This map shows the geographic impact of Nathan Ratliff'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 Nathan Ratliff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Ratliff more than expected).
This network shows the impact of papers produced by Nathan Ratliff. 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 Nathan Ratliff. The network helps show where Nathan Ratliff may publish in the future.
Co-authorship network of co-authors of Nathan Ratliff
This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Ratliff.
A scholar is included among the top collaborators of Nathan Ratliff 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 Nathan Ratliff. Nathan Ratliff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Li, Anqi, Harish Ravichandar, Mustafa Mukadam, et al.. (2019). Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations.. 1457–1468.6 indexed citations
Seo, Young‐Woo, Nathan Ratliff, & Chris Urmson. (2009). Self-supervised aerial image analysis for extracting parking lot structure. International Joint Conference on Artificial Intelligence. 1837–1842.8 indexed citations
17.
Ratliff, Nathan, Matt Zucker, J. Andrew Bagnell, & Siddhartha S Srinivasa. (2009). CHOMP: Gradient optimization techniques for efficient motion planning. Figshare. 489–494.650 indexed citations breakdown →
Ratliff, Nathan, J. Andrew Bagnell, & Martin Zinkevich. (2007). Approximate) Subgradient Methods for Structured Prediction. International Conference on Artificial Intelligence and Statistics. 380–387.22 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.