Edmond Q. Wu | Searcharxiv

arXiv2025

Large language model-based task planning for service robots: A review

With the rapid advancement of large language models (LLMs) and robotics, service robots are increasingly becoming an integral part of daily life, offering a wide range of services in complex environments. To deliver these services intelligently and efficiently, robust and accurate task planning capabilities are essential. This paper presents a comprehensive overview of the integration of LLMs into service robotics, with a particular focus on their role in enhancing robotic task planning. First, the development and foundational techniques of LLMs, including pre-training, fine-tuning, retrieval-augmented generation (RAG), and prompt engineering, are reviewed. We then explore the application of LLMs as the cognitive core-`brain'-of service robots, discussing how LLMs contribute to improved autonomy and decision-making. Furthermore, recent advancements in LLM-driven task planning across various input modalities are analyzed, including text, visual, audio, and multimodal inputs. Finally, we summarize key challenges and limitations in current research and propose future directions to advance the task planning capabilities of service robots in complex, unstructured domestic environments. This review aims to serve as a valuable reference for researchers and practitioners in the fields of artificial intelligence and robotics.

cs.RO↗

arXiv2020

A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors

With the development of research on novel memristor model and device, neural networks by integrating various memristor models have become a hot research topic recently. However, state-of-the-art works still build such neural networks using drift memristor only. Furthermore, some other related works are only applied to a few individual applications including pattern recognition and edge detection. In this paper, a novel kind of multilayer neural network is proposed, in which diffusion and drift memristor models are applied to construct a system merging image preprocessing and pattern recognition. Specifically, the entire network consists of two diffusion memristive cellular layers for image preprocessing and one drift memristive feedforward layer for pattern recognition. Experimental results show that good recognition accuracy of noisy MNIST is obtained due to the fusion of image preprocessing and pattern recognition. Moreover, owing to high-efficiency in-memory computing and brief spiking encoding methods, high processing speed, high throughput, and few hardware resources of the entire network are achieved.

cs.ET↗