Marc Kammer
Papers
1
Total Citations
3
H-Index
1
About
Marc Kammer is a researcher in cognitive robotics and human-robot interaction, with a focus on how robots can learn from and adapt to their environments through perceptual memory systems. His most-cited work, "A Perceptual Memory System for Affordance Learning in Humanoid Robots" (2011), introduces a framework that enables robots to store and retrieve sensory experiences, allowing them to recognize action possibilities—or affordances—in real-world settings. This contribution bridges the gap between low-level perception and high-level reasoning, offering a foundation for more autonomous and adaptive robotic behavior. Though his citation count is modest, with this paper garnering 3 citations, Kammer's work is notable for its early integration of memory and learning in humanoid platforms, a precursor to later advances in developmental robotics. His research underscores the importance of embodied cognition, where robots learn through interaction rather than pre-programmed rules, making his contributions valuable for students and researchers exploring the intersection of artificial intelligence, robotics, and cognitive science.
Research Focus
Key Achievements
Top Papers
- 1A Perceptual Memory System for Affordance Learning in Humanoid Robots3 citations · 2011