Papers
8
Total Citations
71
H-Index
5
About
Parthan Olikkal is a rising researcher at the forefront of human–robot interaction (HRI), with a particular focus on biomimetic control and intuitive communication. His work centers on how robots can learn and replicate natural human gestures—especially hand movements—by leveraging the concept of *synergies*, or movement primitives derived from motor neuroscience. By extracting these synergies from dance and everyday gestures, Olikkal has pioneered methods that allow humanoid robots to learn hand gestures more naturally and efficiently, bridging the gap between human motor control and robotic actuation. His most cited paper, “Emerging Frontiers in Human–Robot Interaction” (2024, 26 citations), surveys the multimodal channels—sight, touch, speech, and learning—that enable effective collaboration. In “Biomimetic learning of hand gestures in a humanoid robot” (2024, 12 citations) and “Musculoskeletal Synergies in the Grasping Hand” (2022, 11 citations), he advances the understanding of how the central nervous system coordinates complex hand movements, translating these insights into robotic platforms. More recently, Olikkal has explored hybrid brain-computer interfaces, as in “A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers” (2024, 5 citations), pushing toward assistive technologies for rehabilitation. With a growing citation record and contributions spanning foundational synergy studies to cutting-edge AI-driven control, Olikkal is shaping the next generation of collaborative, human-aware robots.
Research Focus
Key Achievements
Top Papers
- 1Emerging Frontiers in Human–Robot Interaction26 citations · 2024
- 2Biomimetic learning of hand gestures in a humanoid robot12 citations · 2024
- 3Musculoskeletal Synergies in the Grasping Hand11 citations · 2022
- 4Learning Hand Gestures using Synergies in a Humanoid Robot6 citations · 2023
- 5
- 6
- 7New Horizons in Human–Robot Interaction: Synergy, Cognition, and Emotion4 citations · 2024
- 8