Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds

Abstract:We present a conceptual framework for training Vision-Language Models (VLMs) to perform Visual Perspective Taking (VPT), a core capability for embodied cognition essential for Human-Robot Interaction (HRI). As a first step toward this goal, we introduce a synthetic dataset, generated in NVIDIA Omniverse, that enables supervised learning for spatial reasoning tasks. Each instance includes an RGB image, a natural language description, and a ground-truth 4X4 transformation matrix representing object pose. We focus on inferring Z-axis distance as a foundational skill, with future extensions targeting full 6 Degrees Of Freedom (DOFs) reasoning. The dataset is publicly available to support further research. This work serves as a foundational step toward embodied AI systems capable of spatial understanding in interactive human-robot scenarios.

Artificial Intelligence (cs.AI); Robotics (cs.RO)

arXiv:2505.14366 [cs.AI]

(or arXiv:2505.14366v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2505.14366 Focus to learn more arXiv-issued DOI via DataCite

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