Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review

• URL: http://arxiv.org/abs/2505.20503v1
• Date: Mon, 26 May 2025 20:08:09 GMT
• Title: Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review
• Authors: Matthew Lisondra, Beno Benhabib, Goldie Nejat,
• Abstract summary: We present the first systematic review of the integration of foundation models in mobile service robotics.<n>We explore the role of such models in enabling real-time sensor fusion, language-conditioned control, and adaptive task execution.<n>We also discuss real-world applications in the domestic assistance, healthcare, and service automation sectors.
• Score: 4.540236408836132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action Models have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent systems perceive, reason, and act through physical interactions, robots can improve understanding, adapt to, and execute complex tasks in dynamic real-world environments. However, embodied AI in mobile service robots continues to face key challenges, including multimodal sensor fusion, real-time decision-making under uncertainty, task generalization, and effective human-robot interactions (HRI). In this paper, we present the first systematic review of the integration of foundation models in mobile service robotics, identifying key open challenges in embodied AI and examining how foundation models can address them. Namely, we explore the role of such models in enabling real-time sensor fusion, language-conditioned control, and adaptive task execution. Furthermore, we discuss real-world applications in the domestic assistance, healthcare, and service automation sectors, demonstrating the transformative impact of foundation models on service robotics. We also include potential future research directions, emphasizing the need for predictive scaling laws, autonomous long-term adaptation, and cross-embodiment generalization to enable scalable, efficient, and robust deployment of foundation models in human-centric robotic systems.

Related papers

• Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction [0.4786416643636131]
  Foundation models, including large language models (LLMs) and vision-language models (VLMs) have recently enabled novel approaches to robot autonomy and human-robot interfaces.<n>In parallel, vision-language-action models (VLAs) or large behavior models (BLMs) are increasing the dexterity and capabilities of robotic systems.
  arXiv  Detail & Related papers  (2025-08-07T11:48:03Z)
• A roadmap for AI in robotics [55.87087746398059]
  We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
  arXiv  Detail & Related papers  (2025-07-26T15:18:28Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [66.44234034282421]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Vision-Language-Action Models: Concepts, Progress, Applications and Challenges [4.180065442680541]
  Vision-Language-Action models aim to unify perception, natural language understanding, and embodied action within a single computational framework.<n>This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language-Action models.<n>Key progress areas include architectural innovations, parameter-efficient training strategies, and real-time inference accelerations.
  arXiv  Detail & Related papers  (2025-05-07T19:46:43Z)
• Redefining Robot Generalization Through Interactive Intelligence [0.0]
  We argue that robot foundation models must evolve to an interactive multi-agent perspective in order to handle the complexities of real-time human-robot co-adaptation.<n>By moving beyond single-agent designs, our position emphasizes how foundation models in robotics can achieve a more robust, personalized, and anticipatory level of performance.
  arXiv  Detail & Related papers  (2025-02-09T17:13:27Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Real-World Robot Applications of Foundation Models: A Review [25.53250085363019]
  Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), facilitate flexible application across different tasks and modalities. This paper provides an overview of the practical application of foundation models in real-world robotics.
  arXiv  Detail & Related papers  (2024-02-08T15:19:50Z)
• A Survey on Robotics with Foundation Models: toward Embodied AI [30.999414445286757]
  Recent advances in computer vision, natural language processing, and multi-modality learning have shown that the foundation models have superhuman capabilities for specific tasks. This survey aims to provide a comprehensive and up-to-date overview of foundation models in robotics, focusing on autonomous manipulation and encompassing high-level planning and low-level control.
  arXiv  Detail & Related papers  (2024-02-04T07:55:01Z)
• AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents [109.3804962220498]
  AutoRT is a system to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
  arXiv  Detail & Related papers  (2024-01-23T18:45:54Z)
• Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
  General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks. As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments. Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
  arXiv  Detail & Related papers  (2023-12-14T10:02:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.