Harish Ravichandar

33 papers receiving 705 citations

Hit Papers

Recent Advances in Robot Learning from Demonstration20192026202120232019100200300400

Peers

Harish Ravichandar
Comparison fields: 5 of 68
  • Control and Systems Engineering 446
  • Artificial Intelligence 253
  • Computer Vision and Pattern Recognition 165
  • Biomedical Engineering 138
  • Mechanical Engineering 108
Replace Rudolf Lioutikov with:
Rudolf Lioutikov Germany
Karinne Ramírez-Amaro Germany
Matteo Saveriano Italy
Scott Niekum United States
Chris Paxton United States
Alexis Maldonado Germany
Natsuki Yamanobe Japan
Athanasios Polydoros Denmark
Alberto Finzi Italy
Alexandros Paraschos Germany
Harish Ravichandar relative to Rudolf Lioutikov Germany Rudolf Lioutikov's profile →
Citations per field
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Rudolf Lioutikov · 1×
Citations per year

Countries citing papers authored by Harish Ravichandar

Since Specialization
Citations

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

Fields of papers citing papers by Harish Ravichandar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Harish Ravichandar

This figure shows the co-authorship network connecting the top 25 collaborators of Harish Ravichandar. A scholar is included among the top collaborators of Harish Ravichandar 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 Harish Ravichandar. Harish Ravichandar 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
#WorkIndexed citations
1 3
2 3
3 2
4 14
5 0
6 10
7 4
8
Learning Hierarchical Task Networks with Preferences from Unannotated Demonstrations
1
9 28
10 1
11
Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations.
6
12
Recent Advances in Robot Learning from Demonstrationbreakdown →
432
13 14
14 22
15
Learning Partially Contracting Dynamical Systems from Demonstrations
15
16
Learning and Predicting Sequential Tasks Using Recurrent Neural Networks and Multiple Model Filtering.
5
17
Bayesian human intention inference through multiple model filtering with gaze-based priors
13
18 40
19 4
20 15

About Harish Ravichandar

Harish Ravichandar is a scholar working on Control and Systems Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 34 papers that have together received 721 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (16 papers), Reinforcement Learning in Robotics (6 papers) and Robotic Path Planning Algorithms (5 papers). The work is most often cited by research in Control and Systems Engineering (446 citations), Human-Computer Interaction (55 citations) and Industrial and Manufacturing Engineering (83 citations). Harish Ravichandar has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Sonia Chernova, Athanasios Polydoros, Aude Billard, Ashwin P. Dani, Seth Hutchinson, Nathan Ratliff, Krishna R. Pattipati, Kyle A. Williams, Dieter Fox and Mustafa Mukadam. Their work appears in journals such as The International Journal of Robotics Research, IEEE Control Systems and Autonomous Robots.

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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