As new tactile technologies continually aim to improve the perceptive capabilities of humanoid systems, multiplexed monitoring methods - particularly those using electrical impedance tomography (EIT) hardware - are rapidly gaining popularity in soft robotics. Notably, their potential for system-level adaptability is promising: by multiplexing between different electrodes, the amount and type of information being generated can be altered in real time, balancing accuracy & speed requirements across new environments and tasks. To solidify this approach, we introduce an approach - Economy-of-Touch - which poses tactile sensing as an information-budgeting problem, enabling operating points to be identified across different tasks. We first propose a three-part method of quantifying task complexity, grounded in image-based similarity measures (normalized cross-correlation, Chamfer distance, and edge-based convex hulls) which capture both visual similarity and spatial distribution of the tactile stimuli. Validating our approach with thousands of real-world EIT experiments, we then derive a task-agnostic mapping between this complexity and the required amount of information: the task's operating point. Three tactile tasks (localization, classification, and pattern recognition) are evaluated with real-world experiments, across which we demonstrate operating point MAEs as low as 0.659 channels for previously unseen data.
Hardman et al. (Tue,) studied this question.