Michael Laskey

2.9k total citations
26 papers, 711 citations indexed

About

Michael Laskey is a scholar working on Control and Systems Engineering, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Michael Laskey has authored 26 papers receiving a total of 711 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Control and Systems Engineering, 14 papers in Artificial Intelligence and 7 papers in Biomedical Engineering. Recurrent topics in Michael Laskey's work include Robot Manipulation and Learning (18 papers), Reinforcement Learning in Robotics (12 papers) and Soft Robotics and Applications (6 papers). Michael Laskey is often cited by papers focused on Robot Manipulation and Learning (18 papers), Reinforcement Learning in Robotics (12 papers) and Soft Robotics and Applications (6 papers). Michael Laskey collaborates with scholars based in United States, Switzerland and Sweden. Michael Laskey's co-authors include Ken Goldberg, Jeffrey Mahler, Florian T. Pokorny, Mathieu Aubry, James Kuffner, Kai Kohlhoff, Melrose Roderick, Brian Hou, Torsten Kröger and Anca D. Dragan and has published in prestigious journals such as The International Journal of Robotics Research, arXiv (Cornell University) and eScholarship (California Digital Library).

In The Last Decade

Michael Laskey

25 papers receiving 666 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Laskey United States 15 519 257 223 193 108 26 711
Michael Danielczuk United States 11 582 1.1× 305 1.2× 239 1.1× 135 0.7× 147 1.4× 26 768
Matthew Matl United States 8 584 1.1× 330 1.3× 221 1.0× 147 0.8× 142 1.3× 11 918
Brijen Thananjeyan United States 15 367 0.7× 261 1.0× 169 0.8× 176 0.9× 70 0.6× 25 688
Jeffrey Ichnowski United States 16 393 0.8× 180 0.7× 207 0.9× 83 0.4× 127 1.2× 51 621
Eric Cousineau United States 13 377 0.7× 454 1.8× 190 0.9× 124 0.6× 105 1.0× 18 883
Kaiyu Hang United States 18 638 1.2× 397 1.5× 260 1.2× 133 0.7× 160 1.5× 45 840
Pablo Jiménez Spain 9 552 1.1× 238 0.9× 335 1.5× 138 0.7× 205 1.9× 12 967
Minghao Gou China 8 507 1.0× 246 1.0× 353 1.6× 188 1.0× 53 0.5× 12 783
Daniel Kappler Germany 12 393 0.8× 189 0.7× 198 0.9× 168 0.9× 61 0.6× 20 555
Anis Sahbani France 13 649 1.3× 407 1.6× 318 1.4× 131 0.7× 110 1.0× 48 897

Countries citing papers authored by Michael Laskey

Since Specialization
Citations

This map shows the geographic impact of Michael Laskey'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 Michael Laskey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Laskey more than expected).

Fields of papers citing papers by Michael Laskey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Laskey. 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 Michael Laskey. The network helps show where Michael Laskey may publish in the future.

Co-authorship network of co-authors of Michael Laskey

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Laskey. A scholar is included among the top collaborators of Michael Laskey 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 Michael Laskey. Michael Laskey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shivakumar, Kaushik, Justin Kerr, Brijen Thananjeyan, et al.. (2022). Autonomously Untangling Long Cables. 16 indexed citations
2.
Kollar, Thomas, et al.. (2022). CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation. 2022 International Conference on Robotics and Automation (ICRA). 10632–10640. 43 indexed citations
3.
Sundaresan, Priya, Brijen Thananjeyan, Ashwin Balakrishna, et al.. (2021). Disentangling Dense Multi-Cable Knots. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 3731–3738. 11 indexed citations
4.
Sundaresan, Priya, Brijen Thananjeyan, Ashwin Balakrishna, et al.. (2021). Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies. 16 indexed citations
5.
Lee, Jonathan, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, & Ken Goldberg. (2021). Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning. The International Journal of Robotics Research. 40(10-11). 1284–1305. 2 indexed citations
6.
Bajracharya, Max, James Borders, Thomas Kollar, et al.. (2020). A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes. 11039–11045. 14 indexed citations
7.
Sundaresan, Priya, Brijen Thananjeyan, Ashwin Balakrishna, et al.. (2020). Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data. 9411–9418. 67 indexed citations
8.
Tanwani, Ajay Kumar, Jeffrey Mahler, Michael Laskey, et al.. (2019). Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator. 1373–1379. 6 indexed citations
9.
Lee, Jonathan, Michael Laskey, Roy Fox, & Ken Goldberg. (2018). Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations. 7. 270–277.
10.
Seita, Daniel, Nawid Jamali, Michael Laskey, et al.. (2018). Robot Bed-Making: Deep Transfer Learning Using Depth Sensing of Deformable Fabric.. 10 indexed citations
11.
Wang, David, et al.. (2018). Learning Traffic Behaviors by Extracting Vehicle Trajectories from Online Video Streams. 1276–1283. 15 indexed citations
12.
Laskey, Michael, Jonathan Lee, Richard Liaw, et al.. (2017). Iterative Noise Injection for Scalable Imitation Learning.. arXiv (Cornell University). 5 indexed citations
13.
Chen, Carolyn, Sanjay Krishnan, Michael Laskey, Roy Fox, & Ken Goldberg. (2017). An algorithm and user study for teaching bilateral manipulation via iterated best response demonstrations. 1. 151–158. 2 indexed citations
14.
Laskey, Michael, et al.. (2017). Statistical data cleaning for deep learning of automation tasks from demonstrations. 1142–1149. 10 indexed citations
15.
Laskey, Michael, Jonathan Lee, Jeffrey Mahler, et al.. (2017). Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations. 358–365. 36 indexed citations
16.
17.
Mahler, Jeffrey, Florian T. Pokorny, Brian Hou, et al.. (2016). Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards. 1957–1964. 222 indexed citations
18.
Laskey, Michael, Jonathan Lee, David V. Gealy, et al.. (2016). Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations. Zenodo (CERN European Organization for Nuclear Research). 827–834. 54 indexed citations
19.
Laskey, Michael, Zoe McCarthy, Florian T. Pokorny, et al.. (2015). Multi-armed bandit models for 2D grasp planning with uncertainty. 27 indexed citations
20.
Mahler, Jeffrey, Sanjay Krishnan, Michael Laskey, et al.. (2014). Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression. 532–539. 50 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.

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