arXiv subjects

Guoyuan Zhou

Publications and source records attributed to Guoyuan Zhou.

4 recordsLinked to original sources

arXiv2026

Vision-Based Tactile Sensing for the Perception of the Object's Compliance and Hardness

Object compliance perception enables the identification of soft materials, supporting tasks such as fruit detection and assisted medical palpation. Compliance perception requires sensing an object's deformation and contact forces. Existing vision-based tactile sensing for compliance perception usually depends only on force or deformation. However, deformation-based approaches cannot reliably quantify object compliance, while force-based methods are overly sensitive to geometric variations. To address these limitations, this paper presents a framework that fuses temporal force sequences and deformation field information. Specifically, time-varying contact forces are inferred from tactile image sequences via a neural network, while deformation characteristics are encoded using depth maps. Crucially, the temporal force sequence is used as a dynamic force-response feature for compliance recognition. In standard experiments, the force prediction error reaches 0.06 N within a measurement range of 12 N. The proposed method achieves a 98.0% Shore-hardness classification accuracy for samples ranging from 10 HA to 80 HA. In practical scenarios, including abnormal fruit detection and soft matter, the overall accuracy exceeds 98.5%. This method improves the compliance perception ability of artificial tactile systems and facilitates their deployment in embodied perception applications.

cs.RO↗

arXiv2024

Cyclic Fusion of Measuring Information in Curved Elastomer Contact via Vision-Based Tactile Sensing

Vision-based tactile sensors encode object data via optical signals, capturing microscale deformations using elastomer through densely arranged optical imaging sensors to detect subtle data variations. To enable continuous contact recognition, elastomers are crafted with curved surfaces to adjust to changes in the contact area. However, this design leads to uneven deformations, distorting tactile images and inaccurately reflecting the true elastomer deformations. In this work, we propose a cyclic fusion strategy for vision-based tactile sensing for precise contact data extraction and shape feature integration at the pixel level. Utilizing frequency domain fusion, the system merges topography as indicated by elastomer deformation, enhancing information content by 8%, and regional information bias is reduced by 20% when preserving structural consistency. Further, this system could effectively extract and summarize micro-scale contact features, decreasing erroneous predictions by 20% in defect detection via neural networks and reducing surface projection bias by 50% in surface depth reconstruction. Using this strategy, the measurement minimizes data interference, accurately depicting object morphology on tactile images and enhancing tactile sensation restoration.

cs.RO↗

arXiv2024

Large Language Models Synergize with Automated Machine Learning

Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significant challenges. This paper explores a novel form of program synthesis, targeting ML programs, by combining LLMs and automated machine learning (autoML). Specifically, our goal is to fully automate the generation and optimization of the code of the entire ML workflow, from data preparation to modeling and post-processing, utilizing only textual descriptions of the ML tasks. To manage the length and diversity of ML programs, we propose to break each ML program into smaller, manageable parts. Each part is generated separately by the LLM, with careful consideration of their compatibilities. To ensure compatibilities, we design a testing technique for ML programs. Unlike traditional program synthesis, which typically relies on binary evaluations (i.e., correct or incorrect), evaluating ML programs necessitates more than just binary judgments. Our approach automates the numerical evaluation and optimization of these programs, selecting the best candidates through autoML techniques. In experiments across various ML tasks, our method outperforms existing methods in 10 out of 12 tasks for generating ML programs. In addition, autoML significantly improves the performance of the generated ML programs. In experiments, given the textual task description, our method, Text-to-ML, generates the complete and optimized ML program in a fully autonomous process. The implementation of our method is available at https://github.com/JLX0/llm-automl.

cs.SE↗

arXiv2024

A Vision-Based Tactile Sensing System for Multimodal Contact Information Perception via Neural Network

In general, robotic dexterous hands are equipped with various sensors for acquiring multimodal contact information such as position, force, and pose of the grasped object. This multi-sensor-based design adds complexity to the robotic system. In contrast, vision-based tactile sensors employ specialized optical designs to enable the extraction of tactile information across different modalities within a single system. Nonetheless, the decoupling design for different modalities in common systems is often independent. Therefore, as the dimensionality of tactile modalities increases, it poses more complex challenges in data processing and decoupling, thereby limiting its application to some extent. Here, we developed a multimodal sensing system based on a vision-based tactile sensor, which utilizes visual representations of tactile information to perceive the multimodal contact information of the grasped object. The visual representations contain extensive content that can be decoupled by a deep neural network to obtain multimodal contact information such as classification, position, posture, and force of the grasped object. The results show that the tactile sensing system can perceive multimodal tactile information using only one single sensor and without different data decoupling designs for different modal tactile information, which reduces the complexity of the tactile system and demonstrates the potential for multimodal tactile integration in various fields such as biomedicine, biology, and robotics.

cs.RO↗