Artemiy Oleinikov
Papers
7
Total Citations
96
H-Index
6
About
Artemiy Oleinikov is a robotics researcher whose work sits at the critical intersection of human-robot interaction, safety, and autonomy. His primary research areas include nonlinear model predictive control (NMPC) for physical human-robot interaction, deep learning-based object recognition and pose estimation for pick-and-place operations, and human perception of safety in collaborative robotics. Oleinikov’s most cited work, "Safety-Aware Nonlinear Model Predictive Control for Physical Human-Robot Interaction" (2021, 29 citations), introduces a real-time NMPC framework that guarantees safety by constraining robot motion in shared workspaces—a foundational contribution to safe human-robot coexistence. He also developed a unified deep learning pipeline for object classification and position estimation using RGB-D sensors (2020, 23 citations), advancing autonomous industrial manipulation. His research on perceived safety (2022, 10 citations) provides empirical insights into how different motion planning algorithms affect human comfort and trust, with a user study involving 48 subjects. Additionally, Oleinikov has contributed to bipedal robot balance control and haptic interface design for teleoperation. His work is widely cited in the fields of safe robotics, human-robot collaboration, and autonomous manipulation.
Research Focus
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