Robert A. Hummel

Massachusetts Institute of Technology, Courant Institute of Mathematical Sciences

Papers

Total Citations

H-Index

About

Robert A. Hummel’s research bridges robotics, signal processing, and computer vision, with a focus on efficient spatial sampling and object recognition. His most cited work, “Mission design for compressive sensing with mobile robots” (2011, 22 citations), pioneers the use of compressive sensing theory to guide mobile robots in reconstructing environmental fields from minimal, nonadaptive measurements—a breakthrough that reduces sampling complexity to O(log n). This approach has implications for environmental monitoring and autonomous exploration. Earlier, in “Uncertainty reasoning in object recognition by image processing” (1996, 5 citations), Hummel tackled the challenge of integrating probabilistic reasoning into image-based object identification, laying groundwork for robust vision systems under ambiguous data. His contributions demonstrate a consistent drive to optimize sensing and decision-making in resource-constrained robotic systems. Though his citation counts are modest, Hummel’s work is notable for its theoretical rigor and practical foresight, particularly in applying compressive sensing to mobile robotics before the field’s widespread adoption. His research remains a touchstone for students exploring efficient sensing strategies in autonomous systems.