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Vision-based tactile sensor design using physically based rendering

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.

Periodic cooking of eggs

Egg cooks are challenged by the two-phase structure: albumen and yolk require two cooking temperatures. Separation or a compromise temperature to the detriment of food safety or taste preference are the options. In the present article, we find that it is possible to cook albumen and yolk at two temperatures without separation by using periodic boundary conditions in the energy transport problem. Through mathematical modeling and subsequent simulation, we are able to design the novel cooking method, namely periodic cooking. Comparison with established egg cooking procedures through a plethora of characterization techniques, including Sensory Analysis, Texture Profile Analysis and FT-IR spectroscopy, confirms the different cooking extents and the different variations in protein denaturation with the novel approach. The method not only optimizes egg texture and nutrients, but also holds promise for innovative culinary applications and materials treatment.

Categorizing robots by performance fitness into the tree of robots

Robots are typically classified based on specific morphological features, like their kinematic structure. However, a complex interplay between morphology and intelligence shapes how well a robot performs processes. Just as delicate surgical procedures demand high dexterity and tactile precision, manual warehouse or construction work requires strength and endurance. These process requirements necessitate robot systems that provide a level of performance fitting the process. In this work, we introduce the tree of robots as a taxonomy to bridge the gap between morphological classification and process-based performance. It classifies robots based on their fitness to perform, for example, physical interaction processes. Using 11 industrial manipulators, we constructed the first part of the tree of robots based on a carefully deduced set of metrics reflecting fundamental robot capabilities for various industrial physical interaction processes. Through significance analysis, we identified substantial differences between the systems, grouping them via an expectation-maximization algorithm to create a fitness-based robot classification that is open for contributions and accessible.

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