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About

H. Sutanto is a robotics researcher whose work sits at the intersection of computer vision, visual servoing, and robot motion planning. Over the course of his career, spanning the mid-1990s through the early 2000s, Sutanto made significant contributions to the challenge of enabling robots to operate intelligently using visual feedback, particularly in uncalibrated or real-world settings. His most recognized contributions — each garnering 41 citations — address two foundational problems: autonomous docking using image-based control without requiring camera calibration, and a principled framework for motion planning that incorporates sensor constraints. The former introduced dynamic estimation of the image Jacobian as a practical workaround to the burdensome calibration requirements that had long limited visual servoing applications. His 1997 motion planning framework was notably forward-thinking in unifying sensory data properties with trajectory generation, a theme he revisited and refined in subsequent work on configuration and sensor space integration. Sutanto also contributed methodologically through performance evaluation metrics for image features in visual servo systems, offering tools for more principled feature selection. Across approximately 130 total citations, his body of work reflects a sustained effort to make vision-based robot control more robust, practical, and deployable in dynamic, real-world environments — concerns that remain highly relevant in modern robotics research.