Ulrich Scheller
Papers
2
Total Citations
4
H-Index
2
About
Ulrich Scheller is a researcher in robotics and artificial intelligence, with a focus on autonomous learning and multi-robot systems. His work centers on integrating imitation learning with reinforcement learning to enhance robot autonomy, particularly in dynamic, multi-agent environments. Scheller’s major contributions include pioneering approaches to sporadic imitation—where robots learn from demonstrators in non-fixed, real-time scenarios—moving beyond traditional static demonstrator-imitator setups. His 2009 papers, "Integrating Sporadic Imitation in Reinforcement Learning Robots" and "Increasing the Autonomy of Mobile Robots by Imitation in Multi-robot Scenarios," each with 2 citations, lay groundwork for reducing exploration space and accelerating learning through adaptive imitation. While citation counts are modest, these studies address critical gaps in scalable robot learning, proposing methods that allow robots to autonomously select and learn from demonstrators in unstructured settings. Scheller’s work is notable for its early exploration of flexible imitation frameworks, contributing to the development of more independent and collaborative robotic systems. His research remains relevant for advancing autonomous agents in complex, real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Integrating sporadic imitation in Reinforcement Learning robots2 citations · 2009
- 2