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.
Learning and Reproduction of Gestures by Imitation
2010318 citationsSylvain Calinon, Eric L. Sauser et al.IEEE Robotics & Automation Magazineprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Eric L. Sauser
Since
Specialization
Citations
This map shows the geographic impact of Eric L. Sauser'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 Eric L. Sauser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric L. Sauser more than expected).
This network shows the impact of papers produced by Eric L. Sauser. 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 Eric L. Sauser. The network helps show where Eric L. Sauser may publish in the future.
Co-authorship network of co-authors of Eric L. Sauser
This figure shows the co-authorship network connecting the top 25 collaborators of Eric L. Sauser.
A scholar is included among the top collaborators of Eric L. Sauser 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 Eric L. Sauser. Eric L. Sauser is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Calinon, Sylvain, et al.. (2010). Learning and Reproduction of Gestures by Imitation. IEEE Robotics & Automation Magazine. 17(2). 44–54.318 indexed citations breakdown →
9.
Argall, Brenna, Eric L. Sauser, & Aude Billard. (2009). Demonstration, Tactile Correction and Multiple Training Data Sources for Robot Motion Control. neural information processing systems.1 indexed citations
10.
Argall, Brenna, Eric L. Sauser, & Aude Billard. (2009). Tactile Correction and Multiple Training Data Sources for Robot Motion Control. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 indexed citations
11.
Hersch, Micha, Eric L. Sauser, & Aude Billard. (2008). ONLINE LEARNING OF THE BODY SCHEMA. International Journal of Humanoid Robotics. 5(2). 161–181.53 indexed citations
Sauser, Eric L. & Aude Billard. (2005). View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 119–127.6 indexed citations
16.
Sauser, Eric L. & Aude Billard. (2005). Neural Model of the Transformation from Allo-centric to Ego- centric Representation of Motions. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 indexed citations
Sauser, Eric L. & Aude Billard. (2004). Three dimensional frames of reference transformations using gain modulated populations of neurons. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 543–548.1 indexed citations
Chavarriaga, Ricardo, Eric L. Sauser, & Wulfram Gerstner. (2003). Modeling directional firing properties of place cells. Infoscience (Ecole Polytechnique Fédérale de Lausanne).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.