Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds

• URL: http://arxiv.org/abs/2505.14366v1
• Date: Tue, 20 May 2025 13:49:09 GMT
• Title: Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds
• Authors: Joel Currie, Gioele Migno, Enrico Piacenti, Maria Elena Giannaccini, Patric Bach, Davide De Tommaso, Agnieszka Wykowska,
• Abstract summary: We present a conceptual framework for training Vision-Language Models (VLMs) to perform Visual Perspective Taking (VPT)<n>We introduce a synthetic dataset, generated in NVIDIA Omniverse, that enables supervised learning for spatial reasoning tasks.<n>This work serves as a foundational step toward embodied AI systems capable of spatial understanding in interactive human-robot scenarios.
• Score: 1.696186398088554
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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