Real-World Robot Applications of Foundation Models: A Review
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
Abstract (arXiv)
Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems. The summary encompasses the perspective of input-output relationships in foundation models, as well as their role in perception, motion planning, and control within the field of robotics. This paper concludes with a discussion of future challenges and implications for practical robot applications.
Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 193 of its 241 references that are in the map. First posted on arXiv 8 Feb 2024.
Builds on
193 of 241 references.
Closest ancestors · 9
Same direction, newest first
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Citing papers per year: 2024: 18; 2025: 32; 2026: 10.
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- 2025Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion29 May 2025
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- 2025Experiences from Benchmarking Vision-Language-Action Models for Robotic Manipulation14 Nov 2025
- 2025Leave No Observation Behind: Real-time Correction for VLA Action Chunks27 Sep 2025
- 2025Human Centric General Physical Intelligence for Agile Manufacturing Automation16 Aug 2025
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- 2026System Design for Maintaining Internal State Consistency in Long-Horizon Robotic Tabletop Games26 Mar 2026
- 2026MVR: Multi-view Video Reward Shaping for Reinforcement Learning2 Mar 2026
- 2026E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning27 Jan 2026
- 2025SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding14 Dec 2025
- 2025One Patch is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues25 Nov 2025
- 2025Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation24 Nov 2025
- 2025HARMONIC: A Content-Centric Cognitive Robotic Architecture16 Sep 2025
- 2025INTENTION: Inferring Tendencies of Humanoid Robot Motion Through Interactive Intuition and Grounded VLM6 Aug 2025
- 2025Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research14 Jun 2025
- 2025UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation10 Jun 2025
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