Ingo Keller

Heriot-Watt University, Heriot-Watt University Malaysia

Papers

Total Citations

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About

Ingo Keller’s research bridges human cognition and robotic perception, with a focus on making human-robot interaction safer and more intuitive. His most cited work, “A framework to estimate cognitive load using physiological data” (60 citations), provides a pioneering method for monitoring mental workload in real time—critical for applications in automation, aerospace, and robotics where operator safety is paramount. By leveraging physiological signals, Keller’s framework enables adaptive systems that can adjust task demands to prevent overload, a contribution with direct implications for human factors engineering. In parallel, Keller has advanced robot vision, particularly in object learning under varying illumination. His studies on the iCub humanoid robot (e.g., “On the Illumination Influence for Object Learning on Robot Companions,” 12 citations) systematically analyze how lighting conditions affect long-term visual perception, a key challenge for companion robots operating in unstructured homes. This work informs robust, real-world object recognition. Keller also explores naturalistic gaze control for humanoid robots, aiming to replicate human conversational cues. Though early-stage, this research underscores his commitment to socially aware robotics. With a career spanning cognitive load theory and perceptual robotics, Keller’s work directly impacts the design of safer, more responsive human-robot systems.