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We deal with the problem of estimating the true measured scalar quantity from the output signal of a sensor that is afflicted with hysteresis and noise. We use a probabilistic, nonparametric sensor model based on heteroscedastic Gaussian processes (GPs), which is trained using a data set of sensor output and ground truth pairs. The inference problem is formulated as state estimation in a dynamical system. We exploit the low dimensionality of the latent state space of the sensor to perform exact probabilistic inference of the measured quantity from a time series of the sensor’s output. Compared with the state-of-the-art assumed density filtering algorithm for GPs, which analytically approximates the posterior by a normal distribution during inference, our method reduces the prediction error by 33% on a data set obtained from a novel flexible tactile sensor based on carbon-black filled elastomers. The proposed model can be applied, but is not limited, to any sensor for which the Preisach model of hysteresis holds. The use of probabilistic modeling and inference not only provides a most likely estimate of the measured quantity but also the corresponding confidence interval.

A miniature robotic device that can fold-up on the spot, accomplish tasks, and disappear by degradation into the environment promises a range of medical applications but has so far been a challenge in engineering. This work presents a sheet that can self-fold into a functional 3D robot, actuate immediately for untethered walking and swimming, and subsequently dissolve in liquid. The developed sheet weighs 0.31 g, spans 1.7 cm square in size, features a cubic neodymium magnet, and can be thermally activated to self-fold. Since the robot has asymmetric body balance along the sagittal axis, the robot can walk at a speed of 3.8 body-length/s being remotely controlled by an alternating external magnetic field. We further show that the robot is capable of conducting basic tasks and behaviors, including swimming, delivering/carrying blocks, climbing a slope, and digging. The developed models include an acetone-degradable version, which allows the entire robot's body to vanish in a liquid. We thus experimentally demonstrate the complete life cycle of our robot: self-folding, actuation, and degrading.