Tohid Alizadeh
Italian Institute of Technology, University of Genoa, Nazarbayev University
Papers
14
Total Citations
381
H-Index
7
About
Tohid Alizadeh is a robotics and machine learning researcher whose work sits at the intersection of robot learning, human-robot interaction, and intelligent control systems. His most significant contributions lie in the domain of robot programming by demonstration (PbD), where he has advanced probabilistic frameworks—particularly task-parameterized Gaussian mixture models and dynamic movement primitives—to enable robots to generalize learned movements across varying environments and partially observable conditions. His foundational work on statistical dynamical systems for skills acquisition in humanoids (148 citations) and extrapolation in task-parameterized movement models (99 citations) has become widely referenced in imitation learning literature, demonstrating lasting influence on how robots adapt trajectories to changing task parameters. Beyond movement learning, Alizadeh has made notable contributions to brain-computer interface (BCI)-driven telepresence robotics, developing systems that empower individuals with severe motor paralysis to interact with the world through robotic embodiments—a deeply impactful application of his technical expertise. More recently, his research has expanded into reinforcement learning and sim-to-real transfer through domain randomization and adaptation (2023), reflecting his engagement with cutting-edge challenges in robust robot deployment. With over 360 cumulative citations, Alizadeh's career charts a coherent progression from foundational imitation learning theory toward practical, human-centered robotic applications.
Research Focus
Key Achievements
Top Papers
- 1Statistical dynamical systems for skills acquisition in humanoids148 citations · 2012
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- 3Learning from demonstrations with partially observable task parameters40 citations · 2014
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