WaveTouch: Active Tactile Sensing Using Vibro-Feedback for Classification of Variable Stiffness and Infill Density Objects

The perception and recognition of the surroundings is one of the essential tasks for a robot. With preliminary knowledge about a target object, it can perform various manipulation tasks such as rolling motion, palpation, and force control. Minimizing possible damage to the sensing system and testing objects during manipulation are significant concerns that persist in existing research solutions. To address this need, we designed a new type of tactile sensor based on the active vibro-feedback for object stiffness classification. With this approach, the classification can be performed during the gripping process, enabling the robot to quickly estimate the appropriate level of gripping force required to avoid damaging or dropping the object. This contrasts with passive vibration sensing, which requires to be triggered by object movement and is often inefficient for establishing a secure grip. The main idea is to observe the received changes in artificially injected vibrations that propagate through objects with different physical properties and molecular structures. The experiments with soft subjects demonstrated higher absorption of the received vibrations, while the opposite is true for the rigid subjects that not only demonstrated low absorption but also enhancement of the vibration signal.

Survey on Vision-Language-Action Models

This paper presents an AI-generated review of Vision-Language-Action (VLA) models, summarizing key methodologies, findings, and future directions. The content is produced using large language models (LLMs) and is intended only for demonstration purposes. This work does not represent original research, but highlights how AI can help automate literature reviews. As AI-generated content becomes more prevalent, ensuring accuracy, reliability, and proper synthesis remains a challenge. Future research will focus on developing a structured framework for AI-assisted literature reviews, exploring techniques to enhance citation accuracy, source credibility, and contextual understanding. By examining the potential and limitations of LLM in academic writing, this study aims to contribute to the broader discussion of integrating AI into research workflows. This work serves as a preliminary step toward establishing systematic approaches for leveraging AI in literature review generation, making academic knowledge synthesis more efficient and scalable.