A Larionenko
Papers
1
Total Citations
7
H-Index
1
About
A. Larionenko is a robotics researcher whose work focuses on the intersection of machine learning and physical human-robot interaction, particularly for anthropomorphic systems. Their key research areas include programming by demonstration, intelligent control of robotic manipulators, and knowledge transfer for autonomous systems. Larionenko’s most cited work, "Basic approaches to programming by demonstration for an anthropomorphic robot" (2020, 7 citations), explores how to build intelligent control systems by using a copying suit to capture human motion data, effectively training robots through supervised learning. This contribution addresses a fundamental challenge in robotics: enabling machines to learn complex tasks from human demonstration rather than requiring explicit programming. By structuring the knowledge transfer process, Larionenko’s work helps bridge the gap between human intuition and robotic precision, making it easier to deploy anthropomorphic robots in real-world settings. While still early in their career, Larionenko’s research lays important groundwork for more intuitive human-robot collaboration, with potential applications in manufacturing, healthcare, and assistive technologies. Their focus on practical, data-driven approaches to robot learning positions them as a promising voice in the growing field of interactive robotics.
Research Focus
Key Achievements
Top Papers
- 1