Electrochemiluminescent tactile visual synapse enabling in situ health monitoring
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High-resolution tactile sensors are very helpful to robots for fine-grained perception and manipulation tasks, but designing those sensors is challenging. This is because the designs are based on the compact integration of multiple optical elements, and it is difficult to understand the correlation between the element arrangements and the sensor accuracy by trial and error. In this work, we introduce the digital design of vision-based tactile sensors using a physically accurate light simulator. The framework modularizes the design process, parameterizes the sensor components, and contains an evaluation metric to quantify a sensor’s performance. We quantify the effects of sensor shape, illumination setting, and sensing surface material on tactile sensor performance using our evaluation metric. The proposed optical simulation framework can replicate the tactile image of the real vision-based tactile sensor prototype without any prior sensor-specific data. Using our approach we can substantially improve the design of a fingertip GelSight sensor. This improved design performs approximately 5 times better than previous state-of-the-art human-expert design at real-world robotic tactile embossed text detection. Our simulation approach can be used with any vision-based tactile sensor to produce a physically accurate tactile image. Overall, our approach enables the automatic design of sensorized soft robots and opens the door for closed-loop co-optimization of controllers and sensors for dexterous manipulation.
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Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood. To address this gap, participants performed a feedback-based association learning task and a 1-back localizer task. Increased hemodynamic responses in posterior medial frontal cortex were found for internal and external origins of memory error evidence, and during post-error encoding success as quantified by subsequent recall of face-associated memories. A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. Representation strength was higher during failed recall and increased during encoding when subsequent recall succeeded. These data enhance our understanding of the neurophysiological mechanisms of adaptive learning by linking the need for learning with increased processing of the relevant stimulus category.
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