Papers
3
Total Citations
6
H-Index
2
About
Markus Knauer is a roboticist and machine learning researcher whose work sits at the intersection of continual learning, robot manipulation, and embodied AI. His research addresses fundamental challenges in enabling robots to learn and adapt over time without forgetting prior knowledge. In his work on RECALL, Knauer introduced a rehearsal-free continual learning approach for object classification, allowing deep neural networks to learn new categories on the fly without storing past data—a critical capability for real-world robotic systems with limited memory. He has also advanced skill acquisition in robotics through interactive incremental learning with local trajectory modulation, enabling robots to generalize manipulation skills from limited demonstrations. More recently, with RACCOON, Knauer tackled explainability in embodied question-answering by grounding VLM responses in state summaries from existing robot modules, bridging the gap between foundation models and interpretable robot behavior. Though early in his career, his papers have already garnered citations for their forward-looking approaches to lifelong learning and human-robot interaction. Knauer’s work is shaping how robots can continuously learn, reason, and explain their actions in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 3RECALL: Rehearsal-free Continual Learning for Object Classification2 citations · 2022