Janelle Blankenburg
Papers
9
Total Citations
62
H-Index
5
About
Janelle Blankenburg is a leading voice in human-robot interaction, whose work bridges the gap between artificial social intelligence and practical multi-robot coordination. Her research centers on three key areas: enabling robots to understand and simulate Theory of Mind, developing distributed architectures for complex task allocation, and teaching robots through natural language. Her most influential work, "Perception of Social Intelligence in Robots Performing False-Belief Tasks" (19 citations), demonstrated that a robot’s ability to pass a false-belief test significantly enhances human perceptions of its animacy and social intelligence—a foundational insight for designing more trustworthy collaborative robots. She also pioneered a distributed control architecture for multi-robot task allocation (14 citations) that handles hierarchical tasks with ordering constraints, and introduced a framework for learning complex tasks directly from verbal instruction (9 citations). Her recent work extends these ideas to heterogeneous human-robot teams, using simulation theory of mind to prevent overlapping actions during dynamic coordination. With a publication record spanning 2017 to 2025, Blankenburg’s contributions are shaping how robots perceive, learn, and collaborate—making them not just tools, but true teammates.
Research Focus
Key Achievements
Top Papers
- 1Perception of Social Intelligence in Robots Performing False-Belief Tasks19 citations · 2019
- 2
- 3Learning of Complex-Structured Tasks from Verbal Instruction9 citations · 2019
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- 6Simple Camera-to-2D-LiDAR Calibration Method for General Use4 citations · 2020
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- 9Simulation theory of mind for heterogeneous human-robot teams1 citations · 2025