Evan Krause
Papers
14
Total Citations
210
H-Index
7
About
Evan Krause is a leading researcher in cognitive robotics, specializing in one-shot learning, human-robot interaction, and natural language understanding. His most significant contribution is pioneering spoken instruction-based one-shot object and action learning within the DIARC (Distributed Integrated Cognition Affect and Reflection) architecture, enabling robots to learn new knowledge from a single verbal command and immediately apply it—a capability previously thought to require extensive training data. His foundational work, "Learning to Recognize Novel Objects in One Shot through Human-Robot Interactions in Natural Language Dialogues" (33 citations), and the landmark "Spoken Instruction-Based One-Shot Object and Action Learning in a Cognitive Robotic Architecture" (42 citations) demonstrate this breakthrough. Krause has also advanced analogical generalization from single exemplars (10 citations) and embodied Bayesian models of word learning (9 citations), addressing how robots can learn language in ambiguous, real-world contexts. His work on multi-level introspection (7 citations) and dynamic gaze behavior (4 citations) further pushes robots toward more autonomous, adaptive, and socially intelligent interactions. With over 200 total citations across his most-cited papers, Krause’s research is foundational for building robots that learn quickly, naturally, and robustly from human partners.
Research Focus
Key Achievements
Top Papers
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- 4Recursive Spoken Instruction-Based One-Shot Object and Action Learning13 citations · 2018
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- 7Incrementally biasing visual search using natural language input7 citations · 2013
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