8 papers
IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance
Jongwoo Park, Kanchana Ranasinghe, Jinhyeok Jang +3
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 lightwe…
LACE: Latent Visual Representation for Cross-Embodiment Learning
Yoo Sung Jang, Kanchana Ranasinghe, Cristina Mata +3
Cross-embodiment learning from human demonstrations is hindered by the visual gap between human and robot embodiments. While self-supervised learning (SSL) backbones encode rich in…
Pixel Motion Diffusion is What We Need for Robot Control
E-Ro Nguyen, Yichi Zhang, Kanchana Ranasinghe +2
We present DAWN (Diffusion is All We Need for robot control), a unified diffusion-based framework for language-conditioned robotic manipulation that bridges high-level motion inten…
Phrase-Instance Alignment for Generalized Referring Segmentation
E-Ro Nguyen, Hieu Le, Dimitris Samaras +1
Generalized Referring expressions can describe one object, several related objects, or none at all. Existing generalized referring segmentation (GRES) models treat all cases alike,…
Pixel Motion as Universal Representation for Robot Control
Kanchana Ranasinghe, Xiang Li, E-Ro Nguyen +3
We present LangToMo, a vision-language-action framework structured as a dual-system architecture that uses pixel motion forecasts as intermediate representations. Our high-level Sy…
Image Translation with Kernel Prediction Networks for Semantic Segmentation
Cristina Mata, Michael S. Ryoo, Henrik Turbell
Semantic segmentation relies on many dense pixel-wise annotations to achieve the best performance, but owing to the difficulty of obtaining accurate annotations for real world data…