Karl Jiang

Papers

Total Citations

209

H-Index

About

Karl Jiang is a leading researcher in human-robot interaction, with a primary focus on learning from demonstration and the design of socially intelligent, non-anthropomorphic robots. His most influential work, "Keyframe-based Learning from Demonstration" (2012), has garnered over 200 citations and introduced a paradigm-shifting approach that allows robots to efficiently learn complex tasks by extracting and generalizing from key moments in human demonstrations, significantly reducing the data and computational burden required for skill acquisition. Jiang also explores the nuanced role of emotion in robotics, as seen in his work on "The interplay of context and emotion for non-anthropomorphic robots" (2010), where he investigates how household robots can appropriately express affect without relying on human-like features, making them more intuitive and trustworthy companions. By bridging practical machine learning with affective computing, Jiang’s contributions are shaping how robots learn from people and how they communicate in everyday environments, advancing both the technical and social dimensions of autonomous systems.