In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera computer vision systems. This centralization poses a risk to system reliability and offers a significant limiting factor to system size. In this work, we introduce a decentralized approach for sensing and in a Distributed Manipulator Systems using Neural Cellular Automata (NCA). Demonstrating a decentralized sensing in a hardware implementation, we present a novel inductive sensor board designed for distributed sensing and evaluate its ability to estimate global object properties, such as the geometric center, through local interactions and computations. Experiments demonstrate that NCA-based sensing networks accurately estimate object position at 0.24 times the inter sensor distance. They maintain resilience under sensor faults and noise, and scale seamlessly across varying network sizes. These findings underscore the potential of local, decentralized computations to enable scalable, fault-tolerant, and noise-resilient object property estimation in DMS

Physical contacts are at the base of each embodied interaction. As for living beings, also robots continuously establish diverse contacts to fulfill their tasks. Over the last decades, one of the bold goals of robotics research has been to provide artificial agents with dexterity and adaptability - typical of biological systems - while interacting with their surroundings. Despite the huge work and the excellent outputs in this field, such capabilities still require hard refinements and studies to be fully delivered on our robots. The scientific contribution to this objective builds upon three pillars: the design of an appropriate embodiment - concerning its morphology, actuation strategy, and sensing technology; feature extraction algorithms from tactile signals to build a perception model of the experience; closed-loop robot control strategies that drive robot decisions according to either raw tactile feedback or perceptual representations.