A reliable, accurate, and yet simple dynamic model is important to analyzing, designing, and controlling hybrid rigid–continuum robots. Such models should be fast, as simple as possible, and user-friendly to be widely accepted by the ever-growing robotics research community. In this study, we introduce two new modeling methods for continuum manipulators: a general reduced-order model (ROM) and a discretized model with absolute states and Euler–Bernoulli beam segments (EBA). In addition, a new formulation is presented for a recently introduced discretized model based on Euler–Bernoulli beam segments and relative states (EBR). We implement these models in a Matlab software package, named TMTDyn, to develop a modeling tool for hybrid rigid–continuum systems. The package features a new high-level language (HLL) text-based interface, a CAD-file import module, automatic formation of the system equation of motion (EOM) for different modeling and control tasks, implementing Matlab C-mex functionality for improved performance, and modules for static and linear modal analysis of a hybrid system. The underlying theory and software package are validated for modeling experimental results for (i) dynamics of a continuum appendage, and (ii) general deformation of a fabric sleeve worn by a rigid link pendulum. A comparison shows higher simulation accuracy (8–14% normalized error) and numerical robustness of the ROM model for a system with a small number of states, and computational efficiency of the EBA model with near real-time performances that makes it suitable for large systems. The challenges and necessary modules to further automate the design and analysis of hybrid systems with a large number of states are briefly discussed.

Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating, and improving the person’s teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here that incorporates the teacher’s understanding of, and influence on, the learner. The proposed model is used to clarify the teacher’s objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments ($n=30$ and $n=36$ , respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how $~169$ –180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.

This paper presents a comparative study of approaches to control robots with variable impedance actuators (VIAs) in ways that imitate the behavior of humans. We focus on problems where impedance modulation strategies are recorded from human demonstrators for transfer to robotic systems with differing levels of heterogeneity, both in terms of the dynamics and actuation. We categorize three classes of approach that may be applied to this problem, namely, 1) direct, 2) feature-based, and 3) inverse optimal approaches to transfer. While the first is restricted to highly biomorphic plants, the latter two are shown to be sufficiently general to be applied to various VIAs in a way that is independent of the mechanical design. As instantiations of such transfer schemes, 1) a constraint-based method and 2) an apprenticeship learning framework are proposed, and their suitability to different problems in robotic imitation, in terms of efficiency, ease of use, and task performance, is characterized. The approaches are compared in simulation on systems of varying complexity, and robotic experiments are reported for transfer of behavior from human electromyographic data to two different variable passive compliance robotic devices.

Muscle activity monitoring or electromyography (EMG) is a useful tool. However, EMG is typically invasive, expensive and difficult to use for untrained users. A possible solution is textile-based surface EMG (sEMG) integrated into clothing as a wearable device. This is, however, challenging due to 1) uncertainties in the electrical properties of conductive threads used for electrodes, 2) imprecise fabrication technologies (e.g., embroidery, sewing), and 3) lack of standardization in design variable selection. This paper, for the first time, provides a design guide for such sensors by performing a thorough examination of the effect of design variables on sEMG signal quality. Results show that imprecisions in digital embroidery lead to a trade-off between low electrode impedance and high manufacturing consistency. An optimum set of variables for this trade-off is identified and tested with sEMG during a variable force isometric grip exercise with n = 12 participants, compared with conventional gel-based electrodes. Results show that thread-based electrodes provide a similar level of sensitivity to force variation as gel-based electrodes with about 90% correlation to expected linear behavior. As proof of concept, jogging leggings with integrated embroidered sEMG are made and successfully tested for detection of muscle fatigue while running on different surfaces.

Until now, design of variable physical impedance actuators (VIAs) has been limited mainly to realising variable stiffness while other components of impedance shaping, such as damping, are either fixed (e.g., with the addition of fixed passive dampers) or modulated with active feedback control schemes. In this work we introduce an actuator that is capable of simultaneous and independent physical damping and stiffness modulation. Using optimal control techniques, we explore how variable physical damping can be exploited in such an actuator in the context of rapid movement. Several numerical simulation results are presented, in addition to an experiment realised on variable impedance robotic hardware.