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 ambidextrous robot grasping policies
2019371 citationsJeffrey Mahler, Michael Danielczuk et al.profile →
Combating COVID-19—The role of robotics in managing public health and infectious diseases
This map shows the geographic impact of Ken Goldberg'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 Ken Goldberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ken Goldberg more than expected).
This network shows the impact of papers produced by Ken Goldberg. 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 Ken Goldberg. The network helps show where Ken Goldberg may publish in the future.
Co-authorship network of co-authors of Ken Goldberg
This figure shows the co-authorship network connecting the top 25 collaborators of Ken Goldberg.
A scholar is included among the top collaborators of Ken Goldberg 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 Ken Goldberg. Ken Goldberg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Thananjeyan, Brijen, Kirthevasan Kandasamy, Ion Stoica, et al.. (2021). Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. International Conference on Machine Learning. 10236–10246.
Danielczuk, Michael, Ashwin Balakrishna, Daniel S. Brown, & Ken Goldberg. (2020). Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. 377–393.2 indexed citations
8.
Thananjeyan, Brijen, Ashwin Balakrishna, Ugo Rosolia, et al.. (2019). Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations.. arXiv (Cornell University).1 indexed citations
9.
Balakrishna, Ashwin, et al.. (2019). On-Policy Robot Imitation Learning from a Converging Supervisor. 24–41.4 indexed citations
10.
Fox, Roy, Richard Shin, Sanjay Krishnan, et al.. (2018). Parametrized Hierarchical Procedures for Neural Programming. International Conference on Learning Representations.7 indexed citations
Liang, Eric, Richard Liaw, Robert Nishihara, et al.. (2018). RLlib: Abstractions for Distributed Reinforcement Learning. International Conference on Machine Learning. 3053–3062.50 indexed citations
13.
Laskey, Michael, Jonathan Lee, Richard Liaw, et al.. (2017). Iterative Noise Injection for Scalable Imitation Learning.. arXiv (Cornell University).5 indexed citations
14.
Mahler, Jeffrey & Ken Goldberg. (2017). Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences. 515–524.87 indexed citations
Liang, Eric, Richard Liaw, Robert Nishihara, et al.. (2017). Ray RLLib: A Composable and Scalable Reinforcement Learning Library. arXiv (Cornell University).49 indexed citations
17.
Cudré-Mauroux, Philippe, et al.. (2006). "Sentry Pallets" for Automated Monitoring of Spatial Constraints. Infoscience (Ecole Polytechnique Fédérale de Lausanne).1 indexed citations
18.
Stappen, A. Frank van der, et al.. (2001). Geometry and Part Feeding.1 indexed citations
19.
Goldberg, Ken. (2000). Introduction: the unique phenomenon of a distance. MIT Press eBooks. 2–20.9 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.