Daniel Stronger
Papers
10
Total Citations
113
H-Index
7
About
Daniel Stronger’s research lies at the intersection of autonomous robotics, sensor and actuator model learning, and multi-robot decision-making under uncertainty. His most significant contribution is the development of unsupervised methodologies that enable mobile robots to autonomously induce and calibrate models of their own sensors and actions—without relying on external training data or well-calibrated feedback. His pioneering work on ASAMI (Autonomous Sensor and Actuator Model Induction) and SCASM (Simultaneous Calibration of Action and Sensor Models) has been foundational for creating truly self-aware and adaptive robotic systems. These contributions, cited over 100 times collectively, have influenced subsequent research in robot self-modeling and robust autonomy. Stronger also explored selective visual attention for object detection on legged robots and introduced a polynomial regression function approximator with automated degree selection, tailored for autonomous agents operating in dynamic environments. His work on comparing bottom-up versus top-down approaches to vision and self-localization further demonstrates his commitment to understanding how robots can integrate perception and action under real-world constraints. Stronger’s research continues to inspire new generations of roboticists seeking to build machines that learn and adapt from their own experience.
Research Focus
Key Achievements
Top Papers
- 1Towards autonomous sensor and actuator model induction on a mobile robot27 citations · 2006
- 2From pixels to multi-robot decision-making: A study in uncertainty21 citations · 2006
- 3Simultaneous Calibration of Action and Sensor Models on a Mobile Robot17 citations · 2006
- 4Selective Visual Attention for Object Detection on a Legged Robot13 citations · 2007
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- 7A Model-Based Approach to Robot Joint Control7 citations · 2005
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- 9A Neural Network-Based Approach to Robot Motion Control4 citations · 2008
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