Tootfinder

Tactile stimulations reduce or promote the segregation of auditory streams: psychophysics and modeling https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012701 "cross-modal synchronization, with carefully timed tactile cues, coul…

Tactile stimulations reduce or promote the segregation of auditory streams: psychophysics and modeling
Auditory stream segregation plays a crucial role in understanding the auditory scene. This study investigates the role of tactile stimulation in auditory stream segregation through psychophysics experiments and a computational model of audio-tactile interactions. We examine how tactile pulses, synchronized with one group of tones (high- or low-frequency tones) in a sequence of interleaved high- and low-frequency tones (ABA- triplets), influence the likelihood of perceiving integrated or segrega…

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
Jo\~ao Dami\~ao Almeida, Egidio Falotico, Cecilia Laschi, Jos\'e Santos-Victor
https://arxiv.org/abs/2509.14984

The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation
In-hand manipulation tasks, particularly in human-inspired robotic systems, must rely on distributed tactile sensing to achieve precise control across a wide variety of tasks. However, the optimal configuration of this network of sensors is a complex problem, and while the fingertips are a common choice for placing sensors, the contribution of tactile information from other regions of the hand is often overlooked. This work investigates the impact of tactile feedback from various regions of the…

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LLM-Enhanced Linear Autoencoders for Recommendation
Jaewan Moon, Seongmin Park, Jongwuk Lee
https://arxiv.org/abs/2508.13500 https://arxiv.org/pdf/2508.135…

LLM-Enhanced Linear Autoencoders for Recommendation
Large language models (LLMs) have been widely adopted to enrich the semantic representation of textual item information in recommender systems. However, existing linear autoencoders (LAEs) that incorporate textual information rely on sparse word co-occurrence patterns, limiting their ability to capture rich textual semantics. To address this, we propose L3AE, the first integration of LLMs into the LAE framework. L3AE effectively integrates the heterogeneous knowledge of textual semantics and us…

Visuo-Tactile Feedback with Hand Outline Styles for Modulating Affective Roughness Perception
Minju Baeck, Yoonseok Shin, Dooyoung Kim, Hyunjin Lee, Sang Ho Yoon, Woontack Woo
https://arxiv.org/abs/2508.13504

Visuo-Tactile Feedback with Hand Outline Styles for Modulating Affective Roughness Perception
We propose a visuo-tactile feedback method that combines virtual hand visualization and fingertip vibrations to modulate affective roughness perception in VR. While prior work has focused on object-based textures and vibrotactile feedback, the role of visual feedback on virtual hands remains underexplored. Our approach introduces affective visual cues including line shape, motion, and color applied to hand outlines, and examines their influence on both affective responses (arousal, valence) and…

exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation
Yue Xu, Litao Wei, Pengyu An, Qingyu Zhang, Yong-Lu Li
https://arxiv.org/abs/2509.14688

exUMI: Extensible Robot Teaching System with Action-aware Task-agnostic Tactile Representation
Tactile-aware robot learning faces critical challenges in data collection and representation due to data scarcity and sparsity, and the absence of force feedback in existing systems. To address these limitations, we introduce a tactile robot learning system with both hardware and algorithm innovations. We present exUMI, an extensible data collection device that enhances the vanilla UMI with robust proprioception (via AR MoCap and rotary encoder), modular visuo-tactile sensing, and automated cal…

Insight Rumors: A Novel Textual Rumor Locating and Marking Model Leveraging Att_BiMamba2 Network
Bin Ma, Yifei Zhang, Yongjin Xian, Qi Li, Linna Zhou, Gongxun Miao
https://arxiv.org/abs/2508.12574

Insight Rumors: A Novel Textual Rumor Locating and Marking Model Leveraging Att_BiMamba2 Network
With the development of social media networks, rumor detection models have attracted more and more attention. Whereas, these models primarily focus on classifying contexts as rumors or not, lacking the capability to locate and mark specific rumor content. To address this limitation, this paper proposes a novel rumor detection model named Insight Rumors to locate and mark rumor content within textual data. Specifically, we propose the Bidirectional Mamba2 Network with Dot-Product Attention (Att_…

Exploratory Movement Strategies for Texture Discrimination with a Neuromorphic Tactile Sensor
Xingchen Xu, Ao Li, Benjamin Ward-Cherrier
https://arxiv.org/abs/2509.14954 https:/…

Exploratory Movement Strategies for Texture Discrimination with a Neuromorphic Tactile Sensor
We propose a neuromorphic tactile sensing framework for robotic texture classification that is inspired by human exploratory strategies. Our system utilizes the NeuroTac sensor to capture neuromorphic tactile data during a series of exploratory motions. We first tested six distinct motions for texture classification under fixed environment: sliding, rotating, tapping, as well as the combined motions: sliding+rotating, tapping+rotating, and tapping+sliding. We chose sliding and sliding+rotating …

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
Deqing Song, Weimin Yang, Maryam Rezayati, Hans Wernher van de Venn
https://arxiv.org/abs/2508.12435

Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots
While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected two datasets to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representatio…

Tactile Robotics: An Outlook
Shan Luo, Nathan F. Lepora, Wenzhen Yuan, Kaspar Althoefer, Gordon Cheng, Ravinder Dahiya
https://arxiv.org/abs/2508.11261 https://

Tactile Robotics: An Outlook
Robotics research has long sought to give robots the ability to perceive the physical world through touch in an analogous manner to many biological systems. Developing such tactile capabilities is important for numerous emerging applications that require robots to co-exist and interact closely with humans. Consequently, there has been growing interest in tactile sensing, leading to the development of various technologies, including piezoresistive and piezoelectric sensors, capacitive sensors, m…