Daniel Sliwowski
Papers
2
Total Citations
11
H-Index
2
About
Daniel Sliwowski is a roboticist whose research lies at the intersection of manipulation, multimodal perception, and task execution monitoring. His work advances the ability of robots to perform contact-rich assembly and disassembly—a notoriously difficult problem requiring precise force and visual feedback. Sliwowski is the lead author of the REASSEMBLE dataset (2025, 7 citations), a multimodal benchmark designed to drive progress in robotic assembly and disassembly tasks by providing rich sensor data for learning and evaluation. He also introduced ConditionNET (2024, 4 citations), a vision-language approach that learns the preconditions and effects of robot actions directly from data, enabling robust execution monitoring in everyday environments. By moving beyond hand-coded rules, ConditionNET allows robots to detect when actions succeed or fail, a critical capability for real-world deployment. Sliwowski’s work is notable for its practical focus on closing the loop between perception and action, and his contributions are shaping how robots can operate safely and adaptively in unstructured, human-centered settings.
Research Focus
Key Achievements
Top Papers
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