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1. GradTac: Spatio-Temporal Gradient Based Tactile Sensing

   https://doi.org/10.3389/frobt.2022.898075  

   Ganguly, Kanishka; Mantripragada, Pavan; Parameshwara, Chethan M.; Fermüller, Cornelia; Sanket, Nitin J.; Aloimonos, Yiannis (June 2022, Frontiers in Robotics and AI)

   Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck the right balance of anthropomorphic sizes and shapes and accuracy of tactile response measurement. However, this design is plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing mechanism “damping” the measurement values that are hard to model. To this end, we present a spatio-temporal gradient representation on the data obtained from fluid-based tactile sensors, which is inspired from neuromorphic principles of event based sensing. We present a novel algorithm (GradTac) that converts discrete data points from spatial tactile sensors into spatio-temporal surfaces and tracks tactile contours across these surfaces. Processing the tactile data using the proposed spatio-temporal domain is robust, makes it less susceptible to the inherent noise from the fluid based sensors, and allows accurate tracking of regions of touch as compared to using the raw data. We successfully evaluate and demonstrate the efficacy of GradTac on many real-world experiments performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors. Specifically, we use it for tracking tactile input across the sensor’s surface, measuring relative forces, detecting linear and rotational slip, and for edge tracking. We also release an accompanying task-agnostic dataset for the BioTac SP, which we hope will provide a resource to compare and quantify various novel approaches, and motivate further research. 

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2. Tactile Sensing Systems for Tumor Characterization: A Review

   https://doi.org/10.1109/JSEN.2021.3078369  

   Won, Chang-Hee; Lee, Jong-Ha; Saleheen, Firdous (June 2021, IEEE Sensors Journal)
3. Tactile Sensing System and Convolutional Neural Network for Mechanical Property Classification

   https://doi.org/10.1109/LSENS.2023.3310356  

   Oleksyuk, Vira; Rahman, Nazia; Won, Chang-Hee (October 2023, IEEE Sensors Letters)
4. Bioinspired, Multifunctional, Active Whisker Sensors for Tactile Sensing of Mobile Robots

   https://doi.org/10.1109/LRA.2022.3191172  

   Yu, Zhiqiang; Guo, Yue; Su, Jiaji; Huang, Qiang; Fukuda, Toshio; Cao, Changyong; Shi, Qing (October 2022, IEEE Robotics and Automation Letters)
5. Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots

   He, Juntao; Chong, Baxi; Iaschi, Massimiliano; Nienhusser, Vincent R; Ha, Sehoon; Goldman, Daniel (June 2025, Robotics Science and Systems)

   Many-legged elongated robots show promise for reliable mobility on rugged landscapes. However, most studies on these systems focus on planar motion planning without addressing rapid vertical motion. Despite their success on mild rugged terrains, recent field tests reveal a critical need for 3D behaviors (e.g., climbing or traversing tall obstacles). The challenges of 3D motion planning partially lie in designing sensing and control for a complex high-degree-of-freedom system, typically with over 25 degrees of freedom. To address the first challenge regarding sensing, we propose a tactile antenna system that enables the robot to probe obstacles to gather information about their structure. Building on this sensory input, we develop a control framework that integrates data from the antenna and foot contact sensors to dynamically adjust the robot’s vertical body undulation for effective climbing. With the addition of simple, low-bandwidth tactile sensors, a robot with high static stability and redundancy exhibits predictable climbing performance in complex environments using a simple feedback controller. Laboratory and outdoor experiments demonstrate the robot’s ability to climb obstacles up to five times its height. Moreover, the robot exhibits robust climbing capabilities on obstacles covered with shifting, robot-sized random items and those characterized by rapidly changing curvatures. These findings demonstrate an alternative solution to perceive the environment and facilitate effective response for legged robots, paving ways towards future highly capable, low-profile many-legged robots. 

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