Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.

In this section, we discuss some extensions of Section III and expand on the limitations mentioned in Section VI of the main article.

In this paper we provide a method for identifying and temporally localizing tactile force actions from measured force signals. Our key idea is to use the continuous wavelet transform (CWT) with the Complex Morlet wavelet to transform force signals into feature vectors amenable to machine learning algorithms. Our method uses these feature vectors to train a classifier that recognizes different actions. We demonstrate our approach in a system that records human activities with an instrumented set of tongs. Our system successfully identifies a wide range of actions based on a small set of labeled examples.

Tendon driven continuum robots have attracted a significant amount of attention in the last decade. As a result several discrete and continuous modeling approaches have been explored in order to understand their behavior. The following article proposes an analytical model describing the influence of frictional effects between the tendon and its conduit in tendon driven continuum robotic manipulators. Existing constant curvature models ignore internal device friction completely and treat it as a negligible effect. This results in an assumption that the tendon tension is always constant along the length of the continuum manipulator. While this assumption is reasonable for small articulation angles, internal device friction can significantly change internal tension distribution deteriorating performance of constant curvature models at high articulation angles. The proposed model departs from the constant tension assumption and predicts a tension distribution along the tendon. From the tension distribution a curvature distribution is determined. It is observed that the proposed nonlinear friction model performs better than the existing linear elastic constant curvature model. Following this, a consequence of internal device friction: hysteresis effects are predicted. An algorithm that estimates tendon tension distribution given applied tendon tension history is proposed.