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.
Efficient Bipedal Robots Based on Passive-Dynamic Walkers
This map shows the geographic impact of Russ Tedrake'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 Russ Tedrake with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Russ Tedrake more than expected).
This network shows the impact of papers produced by Russ Tedrake. 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 Russ Tedrake. The network helps show where Russ Tedrake may publish in the future.
Co-authorship network of co-authors of Russ Tedrake
This figure shows the co-authorship network connecting the top 25 collaborators of Russ Tedrake.
A scholar is included among the top collaborators of Russ Tedrake 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 Russ Tedrake. Russ Tedrake is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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
8.
Sinha, Aman, Matthew O’Kelly, Russ Tedrake, & John C. Duchi. (2020). Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems. Neural Information Processing Systems. 33. 6402–6416.1 indexed citations
9.
Li, Yunzhu, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, & Antonio Torralba. (2018). Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids. DSpace@MIT (Massachusetts Institute of Technology).26 indexed citations
10.
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
11.
Tedrake, Russ, et al.. (2017). Verifying Neural Networks with Mixed Integer Programming. arXiv (Cornell University).32 indexed citations
Posa, Michael, et al.. (2013). A direct method for trajectory optimization of rigid bodies through contact. The International Journal of Robotics Research. 33(1). 69–81.382 indexed citations breakdown →
Manchester, Ian R., et al.. (2009). Stable dynamic walking over rough terrain: theory and experiment. Cambridge University Engineering Department Publications Database.20 indexed citations
19.
Roberts, John W. & Russ Tedrake. (2008). Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms. Neural Information Processing Systems. 21. 1361–1368.6 indexed citations
20.
Lent, Michael van, et al.. (1999). Intelligent agents in computer games. National Conference on Artificial Intelligence. 929–930.46 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.