Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand object’s state requires learning object-tactile sensing data relationships. The human somatosensory system combines visual and tactile sensing information in their “What and Where” subsystem to achieve high levels of manipulation skills. The present paper proposes an approach for estimating the pose of in-hand objects combining tactile sensing data and visual frames of reference like the human “What and Where” subsystem. The system proposed here uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of underactuated fingers. While tactile sensing provides local information about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. A dual fuzzy logic controller was developed to achieve and sustain stable grasping conditions autonomously while forces were applied to in-hand objects to expose the system to different object configurations. Two sets of experiments were used to explore the system capabilities. On the first set, external forces changed the orientation of objects while the fuzzy controller kept objects in-hand for tactile and visual data collection for five machine learning estimators. Among these estimators, the ridge regressor achieved an average mean squared error of ${0.077}^{\circ }$ . On the second set of experiments, one of the underactuated fingers performed open-loop object rotations and data recorded were supplied to the same set of estimators. In this scenario, the Multilayer perceptron (MLP) neural network achieved the lowest mean squared error of ${0.067}^{\circ }$ .

Reproducing human-like dexterous manipulation in robots requires identifying objects and textures. In unstructured settings, robots equipped with tactile sensors may detect textures by using touch-related characteristics. An extensive dataset of the physical interaction between a tactile-enable robotic probe is required to investigate and develop methods for categorizing textures. Therefore, this motivates us to compose a dataset from the signals of a bioinspired multimodal tactile sensing module while a robotic probe brings the module to dynamically contact 12 tactile textures under three exploratory velocities. This dataset contains pressure, acceleration, angular rate, and magnetic field variation signals from sensors embedded in the compliant structure of the sensing module. The pressure signals were sampled at 350 Hz, while the signals of the other sensors were sampled at 1500 Hz. Each texture was explored 100 times for each exploratory velocity, and each exploratory episode consisted of a sliding motion in the x and y directions tangential to the surface where the texture is placed. The total number of exploratory episodes in the dataset is 3600. The tactile texture dataset can be used for any project in the area of object recognition and robotic manipulation, and it is especially well suited for tactile texture reconstruction and recognition tasks. The dataset can also be used to study anisotropic textures and how robotic tactile exploration has to consider sliding motion directions.

Momentum has been a consistently studied aspect of sports science for decades. Among the established literature, there has, at times, been a discrepancy between conclusions. However, if momentum is indeed an actual phenomenon, it would affect all aspects of sports, from player evaluation to pre-game prediction and betting. Therefore, using momentum-based features that quantify a team’s linear trend of play, we develop a data pipeline that uses a small sample of recent games to assess teams’ quality of play and measure the predictive power of momentum-based features versus the predictive power of more traditional frequency-based features across several leagues using several machine learning techniques. More precisely, we use our pipeline to determine the differences in the predictive power of momentum-based features and standard statistical features for the National Hockey League (NHL), National Basketball Association (NBA), and five major first-division European football leagues. Our findings show little evidence that momentum has superior predictive power in the NBA. Still, we found some instances of the effects of momentum on the NHL that produced better pre-game predictors, whereas we view a similar trend in European football/soccer. Our results indicate that momentum-based features combined with frequency-based features could improve pre-game prediction models and that, in the future, momentum should be studied more from a feature/performance indicator point-of-view and less from the view of the dependence of sequential outcomes, thus attempting to distance momentum from the binary view of winning and losing.

Dexterous robotic manipulation tasks depend on estimating the state of in-hand objects, particularly their orientation. Although cameras have been traditionally used to estimate the object’s pose, tactile sensors have recently been studied due to their robustness against occlusions. This paper explores tactile data’s temporal information for estimating the orientation of grasped objects. The data from a compliant tactile sensor were collected using different time-window sample sizes and evaluated using neural networks with long short-term memory (LSTM) layers. Our results suggest that using a window of sensor readings improved angle estimation compared to previous works. The best window size of 40 samples achieved an average of 0.0375 for the mean absolute error (MAE) in radians, 0.0030 for the mean squared error (MSE), 0.9074 for the coefficient of determination (R${}^2$), and 0.9094 for the explained variance score (EXP), with no enhancement for larger window sizes. This work illustrates the benefits of temporal information for pose estimation and analyzes the performance behavior with varying window sizes, which can be a basis for future robotic tactile research. Moreover, it can complement underactuated designs and visual pose estimation methods.

This paper discusses an experimental study - from a human-centered perspective - of object recognition through in-hand manipulation by operators using tactile-enabled prosthetic fingertips equipped with multimodal cutaneous tactile sensors and a feedback glove to interactively convey force, vibration, and temperature feedback to the human operator's hand. Results of experiments involving passive and active touch conducted by five human subjects revealed that such a system increases operator's ability to efficiently perform object recognition tasks.