Perception using whisker-inspired tactile sensors currently faces a major challenge: the lack of active control in robots based on direct contact information from the whisker. To accurately reconstruct object contours, it is crucial for the whisker sensor to continuously follow and maintain an appropriate relative touch pose on the surface. This is especially important for localization based on tip contact, which has a low tolerance for sharp surfaces and must avoid slipping into tangential contact. In this paper, we first construct a magnetically transduced whisker sensor featuring a compact and robust suspension system composed of three flexible spiral arms. We develop a method that leverages a characterized whisker deflection profile to directly extract the tip contact position using gradient descent, with a Bayesian filter applied to reduce fluctuations. We then propose an active motion control policy to maintain the optimal relative pose of the whisker sensor against the object surface. A B-Spline curve is employed to predict the local surface curvature and determine the sensor orientation. Results demonstrate that our algorithm can effectively track objects and reconstruct contours with sub-millimeter accuracy. Finally, we validate the method in simulations and real-world experiments where a robot arm drives the whisker sensor to follow the surfaces of three different objects.

Tactile sensing and the manipulation of delicate objects are critical challenges in robotics. This study presents a vision-based magnetic-actuated whisker array sensor that integrates these functions. The sensor features eight whiskers arranged circularly, supported by an elastomer membrane and actuated by electromagnets and permanent magnets. A camera tracks whisker movements, enabling high-resolution tactile feedback. The sensor's performance was evaluated through object classification and grasping experiments. In the classification experiment, the sensor approached objects from four directions and accurately identified five distinct objects with a classification accuracy of 99.17% using a Multi-Layer Perceptron model. In the grasping experiment, the sensor tested configurations of eight, four, and two whiskers, achieving the highest success rate of 87% with eight whiskers. These results highlight the sensor's potential for precise tactile sensing and reliable manipulation.

Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are mounted along the robot$'$s exterior to sense its surroundings through light, non-intrusive contacts. For contact tracking, we employ a sim-to-real learning framework, which involves extensive data collection in simulation followed by a sim-to-real calibration process to transfer the model trained in simulation to the real world. Experiments with whiskers immersed in water indicate that our approach can track contact points with an accuracy of $<2$ mm, without requiring precise robot proprioception. We demonstrate that the approach also generalizes to unseen objects.

Whiskers provide a way to sense surfaces in the immediate environment without disturbing it. In this paper we present a method for using highly flexible, curved, passive whiskers mounted along a robot arm to gather sensory data as they brush past objects during normal robot motion. The information is useful both for guiding the robot in cluttered spaces and for reconstructing the exposed faces of objects. Surface reconstruction depends on accurate localization of contact points along each whisker. We present an algorithm based on Bayesian filtering that rapidly converges to within 1\,mm of the actual contact locations. The piecewise-continuous history of contact locations from each whisker allows for accurate reconstruction of curves on object surfaces. Employing multiple whiskers and traces, we are able to produce an occupancy map of proximal objects.

Perceiving the environment through touch is important for robots to reach in cluttered environments, but devising a way to sense without disturbing objects is challenging. This work presents the design and modelling of whisker-inspired sensors that attach to the surface of a robot manipulator to sense its surrounding through light contacts. We obtain a sensor model using a calibration process that applies to straight and curved whiskers. We then propose a sensing algorithm using Bayesian filtering to localize contact points. The algorithm combines the accurate proprioceptive sensing of the robot and sensor readings from the deflections of the whiskers. Our results show that our algorithm is able to track contact points with sub-millimeter accuracy, outperforming a baseline method. Finally, we demonstrate our sensor and perception method in a real-world system where a robot moves in between free-standing objects and uses the whisker sensors to track contacts tracing object contours.

Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. In this study, we present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed excellent classification accuracy for the roughness and hardness of surface textures. We show that the combination of pressure and accelerometer data, collected from a standard machined specimen using the whisker sensor, improves classification accuracy. Further, we experimentally validate that the sensor can classify texture with roughness depths as low as $2.5\mu m$ at an accuracy of $90\%$ or more and segregate materials based on their roughness and hardness. We present a novel metric to consider while designing a whisker sensor to guarantee the quality of texture data acquisition beforehand. The machine learning model performance was validated against the data collected from the laser sensor from the same set of surface textures. As part of our work, we are releasing two-dimensional texture data: roughness and hardness to the research community.