Trustworthy Vision-Language-Action models and embodied agents
JSPS KAKENHI · 26K25580I study how embodied AI systems can perceive, reason, and act safely in changing environments. My research addresses robustness to environmental variation and adversarial inputs, alongside evaluation frameworks for trustworthy Vision-Language-Action models.
Understanding failures and learning robust representations
I investigate why models fail under adversarial perturbations and perceptual ambiguity, and how to make learning and predictions more reliable. My work connects the mechanisms of these failures with robust training, defense, calibration, and generalization across tasks and modalities.
Robust multimodal understanding and evaluation
I develop training methods and benchmarks for vision-language models that understand visual and textual information reliably. My work brings together multimodal adversarial defense, language supervision, image-text alignment, and evaluation of when models should read or ignore text.
Protecting models and visual content from unauthorized use
I develop methods to establish model ownership and protect visual content against unauthorized AI processing. My work explores fingerprints resilient to model merging, traceable watermarks for image editing, and content-aware perturbations that prevent unwanted colorization.
科研費・研究活動スタート支援
信頼できるVLAの実現に向けた安全評価基盤の構築
Building a Safety Evaluation Framework for Trustworthy Vision-Language-Action Models
日本学術振興会 特別研究員（DC2）
JST AIPチャレンジプログラム