Papers
7
Total Citations
52
H-Index
5
About
Patrick Mania is a robotics and computer vision researcher whose work centers on perception systems for autonomous mobile manipulation, self-supervised learning, and knowledge-driven robot cognition. His most influential contribution, "A Framework for Self-Training Perceptual Agents in Simulated Photorealistic Environments" (2019, 21 citations), addresses one of the field's core bottlenecks: the scarcity of labeled training data for robotic perception, proposing a simulation-based pipeline that allows agents to generate their own supervisory signal. This theme of scalable, autonomous perception development continues throughout his career, as seen in his 2016 work on knowledge-augmented object recognition (10 citations), which argues that richer semantic representations are essential for robots operating in complex human environments. Mania has also advanced object pose estimation through uncertainty-aware deep learning pipelines and probabilistic physical reasoning, exemplified by his NaivPhys4RP framework (2022), which pushes robot perception toward more human-like spatial understanding. His practical contributions extend to real-world deployment, including modeling retail store environments with robotic data collection. His open and flexible perception architecture for mobile manipulation further reflects a commitment to accessible, modular robotics research that bridges cutting-edge methodology with real-world applicability.
Research Focus
Key Achievements
Top Papers
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- 3Robots Collecting Data: Modelling Stores7 citations · 2022
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