Yasuhiro Kato — accepted AI-conference papers

2026

R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation

Naoki Morihira, Amal Nahar, Kartik Bharadwaj, Yasuhiro Kato, Akinobu Hayashi, Tatsuya Harada

ICLR 2026poster

A central challenge in image-based Model-Based Reinforcement Learning (MBRL) is to learn representations that distill essential information from irrelevant visual details. While promising, reconstruction-based methods often waste capacity on large task-irrelevant regions. Decoder-free methods instea…

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2026

Unsupervised Domain Adaptation for Robust Imitation Learning under Visual Perturbations

Yasuhiro Kato, Thomas Westfechtel, Jen-Yen Chang, Naoki Morihira, Akinobu Hayashi, Tatsuya Harada +1

ICRA 2026poster

Vision-based robot manipulation systems often suffer from performance degradation under domain shifts in visual inputs. While data augmentation is commonly employed in reinforcement learning, its application in imitation learning remains relatively underexplored. Our preliminary experiments indicate…

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