Andrew Showers
Papers
1
Total Citations
9
H-Index
1
About
Andrew Showers is a researcher whose work lies at the intersection of human-robot interaction, computer vision, and multimodal communication. His most-cited paper, "Pointing Estimation for Human-Robot Interaction Using Hand Pose, Verbal Cues, and Confidence Heuristics" (2018, 9 citations), introduces a novel framework that fuses hand pose estimation with verbal cues and confidence heuristics to improve how robots interpret human pointing gestures. This contribution addresses a critical challenge in intuitive human-robot collaboration—enabling machines to disambiguate deictic references in real time. By integrating multiple sensory channels and probabilistic reasoning, Showers’ approach enhances the robustness of gesture recognition, paving the way for more natural and efficient interactions between humans and autonomous systems. His work is particularly relevant for applications in assistive robotics, manufacturing, and collaborative environments where precise, context-aware communication is essential. While his citation count reflects the emerging nature of this field, the methodological innovation in combining visual and linguistic cues with confidence scoring positions his research as a foundational step toward more perceptive and responsive robotic agents.
Research Focus
Key Achievements
Top Papers
- 1