Soft grippers offer gentle interaction with objects, significantly reducing the risk of damage. Their compliance in both material and structure allows them to adapt to a wide variety of object shapes and sizes. However, the deformable nature of soft grippers poses challenges for integrating precise proprioception and tactile sensing, especially when aiming for large-area high-resolution tactile perception. In nature, the elephant's trunk exemplifies an ideal combination of compliance and tactile sensitivity, enabling it to delicately manipulate diverse objects without causing damage while also exploring its surroundings through touch. Inspired by this, we present EleTac, a soft vision-based tactile gripper that enables safe object grasping via a pinch-like motion and delivers high-resolution full-surface tactile feedback for integrated proprioceptive and exteroceptive sensing. Experimental results demonstrate that even with a simple control strategy, EleTac can robustly grasp and lift a variety of objects, exhibiting strong adaptability and generalization. Furthermore, the seamless integration of grasping and tactile sensing capabilities facilitates practical applications, including exploration and excavation in granular media, as well as adaptive surface following. Overall, EleTac validates the 'manipulator-as-sensor' design philosophy, achieving high-quality tactile feedback without requiring additional sensing modules.

Soft-bodied robots with multimodal sensing capabilities hold promise for versatile and user-friendly robotics. However, seamlessly integrating multiple sensing functionalities into soft artificial skins remains a challenge due to compatibility issues between soft materials and conventional electronics. While vision-based tactile sensing has enabled simple and effective sensor designs for robotic touch, there has been limited exploration of this technique for intrinsic multimodal sensing in large-sized soft robot bodies. To address this gap, this paper introduces a novel vision-based soft sensing technique, named ProTac, capable of operating either in tactile or proximity sensing modes. This vision-based sensing technology relies on a soft functional skin that can actively switch its optical properties between opaque and transparent states. Furthermore, the paper develops efficient learning pipelines for proximity and tactile perceptions, as well as sensing strategies enabled through the timing activation of the two sensing modes. The effectiveness of the soft sensing technology is demonstrated through a soft ProTac link, which can be integrated into newly constructed or existing commercial robot arms. Results suggest that robots integrated with the ProTac link, along with rigorous control formulation can perform safe and purposeful control actions, which enhances human-robot interaction scenarios and facilitates motion control tasks that are challenging to achieve with conventional rigid links. Supplementary video:https://youtu.be/dFgZLUpeWw4Project website:https://quan-luu.github.io/protac-website/

Recent developments in vision-based tactile sensing offer a simple means to enable robots to perceive touch interactions. However, existing sensors are primarily designed for small-scale applications like robotic hands, lacking research on their integration for large-sized robot bodies that can be leveraged for safe human-robot interactions. This paper explores the utilization of the previously-developed vision-based tactile sensing link (called TacLink) with soft skin as a safety control mechanism, which can serve as an alternative to conventional rigid robot links and impact observers. We characterize the behavior of a robot integrated with the soft TacLink in response to collisions, particularly employing a reactive control strategy. The controller is primarily driven by tactile force information acquired from the soft TacLink sensor through a data-driven sim2real learning method. Compared with a standard rigid link, the results obtained from collision experiments also confirm the advantages of our "soft" solution in impact resilience and in facilitating controls that are difficult to achieve with a stiff robot body. This study can act as a benchmark for assessing the efficiency of soft tactile-sensitive skins in reactive collision responses and open new safety standards for soft skin-based collaborative robots in human-robot interaction scenarios.

There is a growing need for vertical takeoff and landing vehicles, including drones, which are safe to use and can adapt to collisions. The risks of damage by collision, to humans, obstacles in the environment, and drones themselves, are significant. This has prompted a search into nature for a highly resilient structure that can inform a design of propellers to reduce those risks and enhance safety. Inspired by the flexibility and resilience of dragonfly wings, we propose a novel design for a biomimetic drone propeller called Tombo propeller. Here, we report on the design and fabrication process of this biomimetic propeller that can accommodate collisions and recover quickly, while maintaining sufficient thrust force to hover and fly. We describe the development of an aerodynamic model and experiments conducted to investigate performance characteristics for various configurations of the propeller morphology and related properties, such as generated thrust force, thrust force deviation, collision force, recovery time, lift-to-drag ratio, and noise. Finally, we design and showcase a control strategy for a drone equipped with Tombo propellers that collides in midair with an obstacle and recovers from collision continuing flying. The results show that the maximum collision force generated by the proposed Tombo propeller is less than two-thirds that of a traditional rigid propeller, which suggests the concrete possibility to employ deformable propellers for drones flying in a cluttered environment. This research can contribute to the morphological design of flying vehicles for agile and resilient performance.

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, calledSimTacLS, 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.

Stable grip of wet, deformable objects is a challenging task for robotic grasping and manipulation, especially for food products' handling. The wet, slippery interfaces between the object and robotic fingers may require larger gripping force, resulting in higher risk of damaging the grasped object. This research aims to evaluate the role of micro-patterned soft pad on enhancement of wet adhesion in grasping a food sample in wet environment. We showcased this scenario with a tofu block 19.6×19.6×15mm3that is soft, and deformable object, gripped by a soft robotic gripper with two fingers. Each fingertip's surface, which directly makes contact with the tofu, was deposited soft pads in two cases: normal pads (flat surface) and a micropatterned pads. The micropatterned pad comprises of 14400 square cells, each cell has four 85 μm edges, surrounded by a channel network with 44 μm in depth. We conducted estimation of grasped force generated by pads in two cases, then verified by actual setup in griping the tofu block. Both estimated and experimental results reveal that the micropatterned pad decreased necessary load acting on the tofu's surface 2.2 times lower than that of the normal one, while maintaining the stability of the grasped tofu. The showcase in this paper supported the potential of micro patterns on soft fingertip in grasping deformable objects in wet environments without complicated control strategy, promising wider applications for robot in service section or food industry.