We introduce Haplets, a wearable, low-encumbrance, finger-worn, wireless haptic device that provides vibrotactile feedback for hand tracking applications in virtual and augmented reality. Haplets are small enough to fit on the back of the fingers and fingernails while leaving the fingertips free for interacting with real-world objects. Through robust physically-simulated hands and low-latency wireless communication, Haplets can render haptic feedback in the form of impacts and textures, and supplements the experience with pseudo-haptic illusions. When used in conjunction with handheld tools, such as a pen, Haplets provide haptic feedback for otherwise passive tools in virtual reality, such as for emulating friction and pressure-sensitivity. We present the design and engineering for the hardware for Haplets, as well as the software framework for haptic rendering. As an example use case, we present a user study in which Haplets are used to improve the line width accuracy of a pressure-sensitive pen in a virtual reality drawing task. We also demonstrate Haplets used during manipulation of objects and during a painting and sculpting scenario in virtual reality. Haplets, at the very least, can be used as a prototyping platform for haptic feedback in virtual reality.

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

Soft robot fabrication by casting liquid elastomer often requires multiple steps of casting or skillful manual labor. We present a novel soft robotic fabrication technique: negshell casting (negative-space eggshell casting), that reduces the steps required for fabrication by introducing 3D-printed thin-walled cores for use in casting that are meant to be left in place instead of being removed later in the fabrication process. Negshell casting consists of two types of cores: sacrificial cores (negshell cores) and structural cores. Negshell cores are designed to be broken into small pieces that have little effect on the mechanical structure of the soft robot, and can be used for creating fluidic channels and bellows for actuation. Structural cores, on the other hand, are not meant to be broken, and are for increasing the stiffness of soft robotic structures, such as endoskeletons. We describe the design and fabrication concepts for both types of cores and report the mechanical characterization of the cores embedded in silicone rubber specimens. We also present an example use-case of negshell casting for a single joint soft robotic finger, along with an experiment to demonstrate how negshell casting concepts can aid in force transmission. Finally, we present real-world usage of negshell casting in a 6 degree-of-freedom three-finger soft robotic gripper, and a demonstration of the gripper in a robotic pick-and-place task. A companion website with further details about fabrication (as well as an introduction to molding and casting for those who are unfamiliar with the terms), engineering file downloads, and experimental data is provided at https://negshell.github.io/.

This paper presents TouchpadAnyWear, a novel family of textile-integrated force sensors capable of multi-modal touch input, encompassing micro-gesture detection, two-dimensional (2D) continuous input, and force-sensitive strokes. This thin (<1.5 mm) and conformal device features high spatial resolution sensing and motion artifact tolerance through its unique capacitive sensor architecture. The sensor consists of a knitted textile compressive core, sandwiched by stretchable silver electrodes, and conductive textile shielding layers on both sides. With a high-density sensor pixel array (25/cm2), TouchpadAnyWear can detect touch input locations and sizes with millimeter-scale spatial resolution and a wide range of force inputs (0.05 N to 20 N). The incorporation of miniature polymer domes, referred to as “poly-islands”, onto the knitted textile locally stiffens the sensing areas, thereby reducing motion artifacts during deformation. These poly-islands also provide passive tactile feedback to users, allowing for eyes-free localization of the active sensing pixels. Design choices and sensor performance are evaluated using in-depth mechanical characterization. Demonstrations include an 8-by-8 grid sensor as a miniature high-resolution touchpad and a T-shaped sensor for thumb-to-finger micro-gesture input. User evaluations validate the effectiveness and usability of TouchpadAnyWear in daily interaction contexts, such as tapping, forceful pressing, swiping, 2D cursor control, and 2D stroke-based gestures. This paper further discusses potential applications and explorations for TouchpadAnyWear in wearable smart devices, gaming, and augmented reality devices.

Soft robot fabrication by casting liquid elastomer often requires multiple steps of casting or skillful manual labor. We present a novel soft robotic fabrication technique: negshell casting (negative-space eggshell casting), that reduces the steps required for fabrication by introducing 3D-printed thin-walled cores for use in casting that are meant to be left in place instead of being removed later in the fabrication process. Negshell casting consists of two types of cores: sacrificial cores (negshell cores) and structural cores. Negshell cores are designed to be broken into small pieces that have little effect on the mechanical structure of the soft robot, and can be used for creating fluidic channels and bellows for actuation. Structural cores, on the other hand, are not meant to be broken, and are for increasing the stiffness of soft robotic structures, such as endoskeletons. We describe the design and fabrication concepts for both types of cores and report the mechanical characterization of the cores embedded in silicone rubber specimens. We also present an example use-case of negshell casting for a single joint soft robotic finger, along with an experiment to demonstrate how negshell casting concepts can aid in force transmission. Finally, we present real-world usage of negshell casting in a 6 degree-of-freedom three-finger soft robotic gripper, and a demonstration of the gripper in a robotic pick-and-place task.