Matteo Tschesche

Papers

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About

Matteo Tschesche is a robotics researcher whose work centers on human-robot interaction, teleoperation, and the transfer of human expertise to autonomous systems. His key research areas include action discretization for complex manufacturing tasks, shared autonomy in industrial robotics, and learning from demonstration. Tschesche’s major contribution lies in developing methods to extract meaningful action primitives from teleoperated robot demonstrations, particularly in the challenging domain of open-die forging—a task requiring dynamic, human-guided precision. His most-cited paper (2020, 6 citations) addresses how decomposing continuous human teleoperation into discrete actions can pave the way for full or shared autonomy in tasks where human experience is indispensable. This work is notable for bridging the gap between manual control and machine learning, enabling robots to learn from skilled operators while retaining flexibility in goal-oriented behaviors. Tschesche’s research has implications for manufacturing, where preserving human intuition in automated processes can improve adaptability and safety. His focus on action discretization offers a practical pathway toward integrating human expertise into robotic systems, making his contributions valuable for students and researchers interested in teleoperation, imitation learning, and industrial automation.