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

arXiv: https://arxiv.org/abs/2505.15685 · HTML: https://mlsanity.com/paper/2505.15685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 21 May 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Built on by (most cited first, 3 of 3)

- [BINDER: Instantly Adaptive Mobile Manipulation with Open-Vocabulary Commands](https://mlsanity.com/paper/2511.22364/) (2025)
- [On the Strengths and Weaknesses of Data for Open-set Embodied Assistance](https://mlsanity.com/paper/2603.04819/) (2026)
- [Detector Confidence Signals Presence Rather Than Occlusion in Cluttered Manipulation](https://mlsanity.com/paper/2607.13361/) (2026)

## Abstract (arXiv)

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.