Stone Tejeda
Papers
1
Total Citations
34
H-Index
1
About
Stone Tejeda is a pioneering researcher in human-robot interaction and autonomous navigation, with a focus on leveraging natural human communication signals to improve robotic decision-making. Their most-cited work, "Using Human-Inspired Signals to Disambiguate Navigational Intentions" (2020, 34 citations), introduces a novel framework that enables robots to interpret subtle, non-verbal cues—such as gaze direction, gestures, and body orientation—to resolve ambiguity in human navigational intent. This contribution bridges the gap between rigid, pre-programmed robotic paths and the fluid, context-dependent nature of human movement, enhancing safety and efficiency in shared spaces like warehouses, hospitals, and autonomous vehicles. Tejeda’s research has been recognized for its practical applications in collaborative robotics, earning them invitations to speak at major conferences on human-robot collaboration. By integrating insights from cognitive science and robotics, their work not only advances autonomous systems but also sets a foundation for more intuitive human-machine partnerships. With growing citation impact, Stone Tejeda is shaping the future of socially aware navigation.
Research Focus
Key Achievements
Top Papers
- 1Using Human-Inspired Signals to Disambiguate Navigational Intentions34 citations · 2020