Papers

37

Total Citations

581

H-Index

14

About

Shivesh Kumar is a robotics researcher whose work spans exoskeleton design, humanoid robotics, and the kinematics and dynamics of series-parallel hybrid robotic systems. His research has made significant contributions to both rehabilitation engineering and advanced robot modeling, establishing him as a versatile voice in modern robotics. Kumar's most-cited work includes the Recupera exoskeleton for post-stroke neurorehabilitation (67 citations) and influential surveys on lower extremity exoskeletons and hybrid robot modularity, each exceeding 50 citations. His development of analytical software workbenches and modular methods for solving complex kinematics and dynamics in series-parallel hybrid robots has provided the field with practical, reusable tools that bridge theoretical modeling and real-world implementation. His design contributions to the RH5 humanoid robot further demonstrate his ability to translate algorithmic insights into functioning physical systems. Beyond rehabilitation and humanoids, Kumar has explored underactuated locomotion through the AcroMonk brachiating robot, reflecting a broad curiosity about bio-inspired motion. With over 350 cumulative citations across his top papers, his work continues to shape how researchers approach modular robot design, decentralized control, and human-robot interaction — making his publications essential reading for anyone entering these fields.

Research Focus

Key Achievements

14
H-Index
37
Papers
581
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation
67 citations · 2019
📈 Most Prolific Year: 2022 (10 Papers)
🤝 Key Collaborators: 71
🏛 Institutions: German Research Centre for Artificial Intelligence, Chalmers University of Technology

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago