Kyle Spurlock
Papers
1
Total Citations
3
H-Index
1
About
Kyle Spurlock is a researcher at the intersection of robotics, human-robot collaboration, and explainable artificial intelligence (XAI). His work focuses on making robotic systems more transparent and trustworthy, particularly in collaborative environments where humans and robots work side by side. In his most-cited paper, "Comparative Analysis of Post Hoc Explainable Methods for Robotic Grasp Failure Prediction" (2025), Spurlock addresses a critical gap: while machine learning models can predict grasp failures with high accuracy, their lack of interpretability hinders deployment in safety-critical settings. By systematically comparing post hoc explanation methods, he provides a framework for understanding why robots fail, enabling more reliable and human-aware automation. This contribution is especially relevant as industries increasingly adopt collaborative robots. Though early in his career, Spurlock’s work has already garnered attention, with his top paper accumulating three citations, signaling growing interest in his approach. His research promises to bridge the gap between high-performing black-box models and the transparency needed for real-world human-robot interaction, making him a promising voice in the future of explainable robotics.
Research Focus
Key Achievements
Top Papers
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