Cristina Mata — accepted AI-conference papers

2026

IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance

Jongwoo Park, Kanchana Ranasinghe, Jinhyeok Jang, Cristina Mata, Yoo Sung Jang, Michael S. Ryoo

ICRA 2026poster

Many Vision-Language-Action (VLA) models flatten image patches into a 1D token sequence, weakening the 2D spatial cues needed for precise manipulation. We introduce IVRA, a lightweight, training-free method that improves spatial understanding by exploiting affinity hints already available in the mod…

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2025

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy

Xiang Li, Cristina Mata, Jongwoo Park, Kumara Kahatapitiya, Yoo Sung Jang, Jinghuan Shang +5

ICLR 2025poster

Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for robotic control remains challenging, particularly when constrained by a limited number of robot demonstrations. In this…

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