3 papers
cs.RO2026
Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
Henrik Krauss, Johann Licher, Naoya Takeishi +2
Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require p…
cs.LG2026
Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games
Henrik Krauss, Takehisa Yairi
We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with sy…
cs.LG2026
Revealing Human Attention Patterns from Gameplay Analysis for Reinforcement Learning
Henrik Krauss, Takehisa Yairi
This study introduces a novel method for revealing human internal attention patterns (decision-relevant attention) from gameplay data alone, leveraging offline attention techniques…