Tyler Frasca
Papers
7
Total Citations
144
H-Index
5
About
Tyler Frasca is a leading researcher in cognitive robotics, with a focus on integrating human-robot interaction, machine learning, and autonomous decision-making. His work centers on enabling robots to learn and adapt through natural language instruction, a key challenge in artificial intelligence. Frasca’s major contributions include pioneering the first demonstration of spoken instruction-based one-shot object and action learning within the Distributed Integrated Cognition Affect and Reflection (DIARC) architecture, allowing robots to acquire new knowledge from a single command and apply it immediately. This work, detailed in his highly cited 2018 overview of DIARC (68 citations) and subsequent studies (42 and 13 citations), has advanced the field of cognitive architectures. He has also developed frameworks for robot self-assessment, enabling machines to estimate task performance before, during, and after missions, as seen in his 2022 paper (8 citations) and related dialogues research (6 citations). Frasca’s notable achievements include empirical demonstrations of task-general cognitive architectures for adaptive performance, highlighting their utility over specialized systems. With over 140 total citations, his research is shaping the future of autonomous robots that can learn, introspect, and collaborate effectively with humans.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Recursive Spoken Instruction-Based One-Shot Object and Action Learning13 citations · 2018
- 4A Framework for Robot Self-Assessment of Expected Task Performance8 citations · 2022
- 5
- 6Enabling Fast Instruction-Based Modification of Learned Robot Skills4 citations · 2021
- 7