Hongkai Dai

2.2k total citations · 1 hit paper
20 papers, 1.3k citations indexed

About

Hongkai Dai is a scholar working on Control and Systems Engineering, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hongkai Dai has authored 20 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Control and Systems Engineering, 10 papers in Biomedical Engineering and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hongkai Dai's work include Robotic Locomotion and Control (9 papers), Robotic Path Planning Algorithms (6 papers) and Prosthetics and Rehabilitation Robotics (6 papers). Hongkai Dai is often cited by papers focused on Robotic Locomotion and Control (9 papers), Robotic Path Planning Algorithms (6 papers) and Prosthetics and Rehabilitation Robotics (6 papers). Hongkai Dai collaborates with scholars based in United States, Switzerland and United Kingdom. Hongkai Dai's co-authors include Russ Tedrake, Andrés Valenzuela, Frank Permenter, Robin Deits, Pat Marion, Scott Kuindersma, Twan Koolen, Maurice Fallon, Marco Pavone and Benoit Landry and has published in prestigious journals such as Physics Letters B, The International Journal of Robotics Research and Autonomous Robots.

In The Last Decade

Hongkai Dai

20 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

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.

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

2015 564 citations Scott Kuindersma, Robin Deits et al. Autonomous Robots profile →

Peers

Hongkai Dai

BE

CSE

CVPR

ME

AI

Andrés Valenzuela United States

Ayonga Hereid United States

Twan Koolen United States

Andrea Del Prete Italy

Michael Posa United States

Zhangguo Yu China

Vassilios Tsounis Switzerland

Adrien Escande France

Farbod Farshidian Switzerland

Gerardo Bledt United States

Andrés Valenzuela United States

Hongkai Dai

793 ×0.8

BE

485 ×0.7

CSE

243 ×0.8

CVPR

133 ×0.8

ME

89 ×0.6

AI

Citations per year, relative to Hongkai Dai Hongkai Dai (= 1×) peers Andrés Valenzuela

Countries citing papers authored by Hongkai Dai

Since Specialization

Citations

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

Fields of papers citing papers by Hongkai Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongkai Dai

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Dai, Hongkai, et al.. (2024). Verification and Synthesis of Compatible Control Lyapunov and Control Barrier Functions. 8178–8185. 1 indexed citations

2.

Dai, Hongkai & Frank Permenter. (2023). Convex synthesis and verification of control-Lyapunov and barrier functions with input constraints. 4116–4123. 18 indexed citations

3.

Dai, Hongkai, et al.. (2023). Certified polyhedral decompositions of collision-free configuration space. The International Journal of Robotics Research. 43(9). 1322–1341. 13 indexed citations

4.

Zhang, Hongchao, et al.. (2023). Efficient Sum of Squares-Based Verification and Construction of Control Barrier Functions by Sampling on Algebraic Varieties. 5384–5391. 3 indexed citations

5.

Dai, Hongkai, et al.. (2023). Approximate Optimal Controller Synthesis for Cart-Poles and Quadrotors via Sums-of-Squares. IEEE Robotics and Automation Letters. 8(11). 7376–7383. 4 indexed citations

6.

Landry, Benoit, Hongkai Dai, & Marco Pavone. (2021). SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions.. 1105–1117. 4 indexed citations

7.

Dai, Hongkai, et al.. (2021). Lyapunov-stable neural-network control. 65 indexed citations

8.

Dai, Hongkai, Benoit Landry, Marco Pavone, & Russ Tedrake. (2020). Counter-example guided synthesis of neural network Lyapunov functions for piecewise linear systems. 1274–1281. 23 indexed citations

9.

Dai, Hongkai, Gregory Izatt, & Russ Tedrake. (2019). Global inverse kinematics via mixed-integer convex optimization. The International Journal of Robotics Research. 38(12-13). 1420–1441. 33 indexed citations

10.

Focchi, Michele, et al.. (2018). Application of Wrench-Based Feasibility Analysis to the Online Trajectory Optimization of Legged Robots. IEEE Robotics and Automation Letters. 3(4). 3363–3370. 36 indexed citations

11.

Dai, Hongkai, et al.. (2017). A novel nuclear dependence of nucleon–nucleon short-range correlations. Physics Letters B. 769. 446–450. 3 indexed citations

12.

Dai, Hongkai, et al.. (2017). A mixed-integer convex optimization framework for robust multilegged robot locomotion planning over challenging terrain. 7 indexed citations

13.

Marion, Pat, Maurice Fallon, Robin Deits, et al.. (2016). Director: A User Interface Designed for Robot Operation with Shared Autonomy. Journal of Field Robotics. 34(2). 262–280. 40 indexed citations

14.

Dai, Hongkai & Russ Tedrake. (2016). Planning robust walking motion on uneven terrain via convex optimization. 69 indexed citations

15.

Kuindersma, Scott, Robin Deits, Maurice Fallon, et al.. (2015). Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Autonomous Robots. 40(3). 429–455. 564 indexed citations breakdown →

16.

Dai, Hongkai, Andrés Valenzuela, & Russ Tedrake. (2014). Whole-body motion planning with centroidal dynamics and full kinematics. 295–302. 265 indexed citations

17.

Dai, Hongkai, et al.. (2014). Whole-body Motion Planning with Simple Dynamics and Full Kinematics. 17 indexed citations

18.

Fallon, Maurice, Scott Kuindersma, Sisir Karumanchi, et al.. (2014). An Architecture for Online Affordance‐based Perception and Whole‐body Planning. Journal of Field Robotics. 32(2). 229–254. 91 indexed citations

19.

Dai, Hongkai & Russ Tedrake. (2013). L<inf>2</inf>-gain optimization for robust bipedal walking on unknown terrain. 3116–3123. 36 indexed citations

20.

Dai, Hongkai & Russ Tedrake. (2012). Optimizing robust limit cycles for legged locomotion on unknown terrain. 1207–1213. 47 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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