Antonis Sidiropoulos
Papers
10
Total Citations
106
H-Index
7
About
Antonis Sidiropoulos is a leading researcher in physical human-robot interaction (pHRI), with a focus on enabling safe and effective collaboration between humans and industrial robots. His work centers on developing control frameworks that allow robots to adapt their kinematic behavior in real-time, particularly through the use of Dynamic Movement Primitives (DMP) and variable admittance control. Sidiropoulos has made major contributions to progressive automation, where repetitive tasks are gradually transferred from human to robot through physical interaction, and to the generalization of DMP under kinematic constraints—a key challenge for online trajectory generation. His most cited paper (20 citations) introduces a framework for automating repetitive tasks via pHRI, while his 2022 work on generalizing DMP under constraints (19 citations) is foundational for adaptive robot motion. He has also advanced safe collaboration with high-payload robots (15 citations), addressing a critical gap in industrial automation. Sidiropoulos’s research on human motion prediction using DMP and haptic cues has further enhanced human-robot object transfer, with notable papers on handover policies and large-inertia object manipulation. His recent work on dynamic via-points and spatial generalization (2024) continues to push the boundaries of DMP-based trajectory generation, solidifying his impact on the future of collaborative robotics.
Research Focus
Key Achievements
Top Papers
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- 9A Reversible Dynamic Movement Primitive formulation2 citations · 2021
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