There is currently a scarcity of wearable robotic devices that can practically provide physical assistance in a range of real world activities. Soft wearable exoskeletons, or exosuits, have the potential to be more portable and less restrictive than their rigid counterparts. In this paper, we present the design of an actuation system that has been optimized for use in a soft exosuit for the human arm. The selected design comprises a DC motor and gearbox, a flexible cable conduit transmission, and a custom series elastic force sensor. Placed in series with the transmission conduit, the custom compliant force sensor consists of a translational steel compression spring with a pair of Hall effect sensors for measuring deflection. The custom sensor is validated as an accurate means of measuring cable tension, and it is shown that it can be used in feedback to control the cable tension with high bandwidth. The dynamic effect of the cable-conduit transmission on the force felt at the user interface is characterized by backdriving the system as it renders a range of virtual impedances to the user. We conclude with recommendations for the integration of such an actuation system into a full wearable exosuit.

Humans can feel fine surface details through a rigid probe by attending to the high-frequency vibrations caused by contact. To aid our efforts in creating realistic virtual textures, we sought to understand the dynamics of such interactions through a combination of data collection and offline simulation. A handheld probe was fitted with interchangeable hemispherical tool tips, accelerometers, a force sensor, and a magnetic motion tracker and then used to record interactions with a sinusoidal grating. We mathematically modeled this system using planar rigid-body dynamics, a lumped-parameter model of the human hand, an offset surface for collision detection, and Hertzian contact mechanics at the point(s) of interaction. Tuning the parameters of the hand enabled the simulation output to closely match the lateral and axial tool accelerations recorded at four combinations of tool tip size and tool speed.

Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.