Recent studies have been inspired by natural whiskers for a proposal of tactile sensing system to augment the sensory ability of autonomous robots. In this study, we propose a novel artificial soft whisker sensor that is not only flexible but also adapts and compensates for being trimmed or broken during operation. In this morphological compensation designed from an analytical model of the whisker, our sensing device actively adjusts its morphology to regain sensitivity close to that of its original form (before being broken). To serve this purpose, the body of the whisker comprises a silicon-rubber truncated cone with an air chamber inside as the medulla layer, which is inflated to achieve rigidity. A small strain gauge is attached to the outer wall of the chamber for recording strain variation upon contact of the whisker. The chamber wall is reinforced by two inextensible nylon fibers wound around it to ensure that morphology change occurs only in the measuring direction of the strain gauge by compressing or releasing pressurized air contained in the chamber. We investigated an analytical model for the regulation of whisker sensitivity by changing the chamber morphology. Experimental results showed good agreement with the numerical results of performance by an intact whisker in normal mode, as well as in compensation mode. Finally, adaptive functionality was tested in two separate scenarios for thorough evaluation: (1) A short whisker (65 mm) compensating for a longer one (70 mm), combined with a special case (selfcompensation), and (2) vice versa. Preliminary results showed good feasibility of the idea and efficiency of the analytical model in the compensation process, in which the compensator in the typical scenario performed with 20.385% average compensation error. Implementation of the concept in the present study fulfills the concept of morphological computation in soft robotics and paves the way toward accomplishment of an active sensing system that overcomes a critical event (broken whisker) based on optimized morphological compensation.

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Large-scale robotic skin with tactile sensing ability is emerging with the potential for use in close-contact human-robot systems. Although recent developments in vision-based tactile sensing and related learning methods are promising, they have been mostly designed for small-scale use, such as by fingers and hands, in manipulation tasks. Moreover, learning perception for such tactile devices demands a huge tactile dataset, which complicates the data collection process. To address this, this study introduces a multiphysics simulation pipeline, called SimTacLS, which considers not only the mechanical properties of external physical contact but also the realistic rendering of tactile images in a simulation environment. The system utilizes the obtained simulation dataset, including virtual images and skin deformation, to train a tactile deep neural network to extract high-level tactile information. Moreover, we adopt a generative network to minimize sim2real inaccuracy, preserving the simulation-based tactile sensing performance. Last but not least, we showcase this sim2real sensing method for our large-scale tactile sensor (TacLink) by demonstrating its use in two trial cases, namely, whole-arm nonprehensile manipulation and intuitive motion guidance, using a custom-built tactile robot arm integrated with TacLink. This article opens new possibilities in the learning of transferable tactile-driven robotics tasks from virtual worlds to actual scenarios without compromising accuracy.

Robots have been brought to work close to humans in many scenarios. For coexistence and collaboration, robots should be safe and pleasant for humans to interact with. To this end, the robots could be both physically soft with multimodal sensing/perception, so that the robots could have better awareness of the surrounding environment, as well as to respond properly to humans' action/intention. This paper introduces a novel soft robotic link, named ProTac, that possesses multiple sensing modes: tactile and proximity sensing, based on computer vision and a functional material. These modalities come from a layered structure of a soft transparent silicon skin, a polymer dispersed liquid crystal (PDLC) film, and reflective markers. Here, the PDLC film can switch actively between the opaque and the transparent state, from which the tactile sensing and proximity sensing can be obtained by using cameras solely built inside the ProTac link. In this paper, inference algorithms for tactile proximity perception are introduced. Evaluation results of two sensing modalities demonstrated that, with a simple activation strategy, ProTac link could effectively perceive useful information from both approaching and in-contact obstacles. The proposed sensing device is expected to bring in ultimate solutions for design of robots with softness, whole-body and multimodal sensing, and safety control strategies.