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arXiv · 2505.15685

From Grounding to Manipulation: Case Studies of Foundation Model Integration in Embodied Robotic Systems

Xiuchao Sui·Daiying Tian·Qi Sun·Ruirui Chen·Dongkyu Choi·Kenneth Kwok·Soujanya Poria
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Abstract

Foundation models (FMs) are increasingly used to bridge language and action in embodied agents, yet the operational characteristics of different FM integration strategies remain under-explored -- particularly for complex instruction following and versatile action generation in changing environments. This paper examines three paradigms for building robotic systems: end-to-end vision-language-action (VLA) models that implicitly integrate perception and planning, and modular pipelines incorporating either vision-language models (VLMs) or multimodal large language models (LLMs). We evaluate these paradigms through two focused case studies: a complex instruction grounding task assessing fine-grained instruction understanding and cross-modal disambiguation, and an object manipulation task targeting skill transfer via VLA finetuning. Our experiments in zero-shot and few-shot settings reveal trade-offs in generalization and data efficiency. By exploring performance limits, we distill design implications for developing language-driven physical agents and outline emerging challenges and opportunities for FM-powered robotics in real-world conditions.

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Xiuchao Sui, Daiying Tian, Qi Sun, Ruirui Chen, Dongkyu Choi, Kenneth Kwok, Soujanya Poria. 2025-05-21. From Grounding to Manipulation: Case Studies of Foundation Model Integration in Embodied Robotic Systems. https://arxiv.org/abs/2505.15685

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