Jeremy Bryan Wright
Papers
1
Total Citations
2
H-Index
1
About
Jeremy Bryan Wright is a researcher whose work lies at the intersection of human-robot interaction and artificial intelligence, with a particular focus on how people naturally teach autonomous systems. His key research areas include multimodal instruction, human-agent collaboration, and the design of intuitive interfaces for robotic learning. Wright’s major contribution is his exploration of how humans instinctively use multiple modes—such as speech, gesture, and demonstration—when instructing others, and how these natural teaching behaviors can be leveraged to make robots and AI agents more accessible. His most-cited paper, "Human Natural Instruction of a Simulated Electronic Student" (2011, 2 citations), lays foundational groundwork by reviewing prior work and presenting observations from experiments on how people teach simulated agents. While his citation count is modest, his work addresses a critical challenge in robotics: moving beyond programming to enable machines that learn through natural human interaction. Wright’s research is particularly valuable for students and researchers interested in creating more intuitive, human-centered AI systems that can be instructed without specialized technical knowledge.
Research Focus
Key Achievements
Top Papers
- 1Human Natural Instruction of a Simulated Electronic Student2 citations · 2011