Edge Computing and its Application in Robotics: A Survey
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Abstract (arXiv)
The Edge computing paradigm has gained prominence in both academic and industry circles in recent years. By implementing edge computing facilities and services in robotics, it becomes a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network's edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2025.
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Citing papers per year: 2025: 2; 2026: 7.
9 papers.
- 2025EdgeNavMamba: Mamba Optimized Object Detection for Energy Efficient Edge Devicescited by 2
- 2026Towards Considerate Embodied AI: Co-Designing Situated Multi-Site Healthcare Robots from Abstract Concepts to High-Fidelity Prototypescited by 2
- 2025Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integrationcited by 0
- 2026vAccSOL: Efficient and Transparent AI Vision Offloading for Mobile Robotscited by 0
- 2026Remote Action Generation: Remote Control with Minimal Communicationcited by 0
- 2026AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Roboticscited by 0
- 2026TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUscited by 0
- 2026Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetsoncited by 0
- 2026Cache-Aware Conv3D Lowering Across Embedded World-Model Decoderscited by 0
- 2026Cache-Aware Conv3D Lowering Across Embedded World-Model Decoders25 Sep 2026
- 2026Bimanual Manipulation Within an 8 GB Budget: Zero-Copy Sensing and Quantized ACT on an Entry-Level Jetson4 Aug 2026
- 2026TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs9 Jun 2026
- 2026AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics31 May 2026
- 2026Remote Action Generation: Remote Control with Minimal Communication3 May 2026
- 2026vAccSOL: Efficient and Transparent AI Vision Offloading for Mobile Robots17 Mar 2026
- 2026Towards Considerate Embodied AI: Co-Designing Situated Multi-Site Healthcare Robots from Abstract Concepts to High-Fidelity Prototypes3 Feb 2026
- 2025Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integration9 Nov 2025
- 2025EdgeNavMamba: Mamba Optimized Object Detection for Energy Efficient Edge Devices16 Oct 2025