Machine-learned Adaptive Switching in Voluntary Lower-limb Exoskeleton Control: Preliminary Results.

Lower-limb exoskeletons utilize fixed control strategies and are not adaptable to user's intention. To this end, the goal of this study was to investigate the potential of using temporal-difference learning and general value functions for predicting the next possible walking mode that will be selected by users wearing exoskeletons in order to reduce the effort and cognitive load while switching between different modes of walking. Experiments were performed with a user wearing the Indego exoskeleton and given the authority to switch between five walking modes that were different in terms of speed and turn direction. The user's switching preferences were learned and predicted from device-centric and room-centric measurements by considering similarities in the movements being performed. A switching list was updated to show the most probable future next modes to be selected by the user. In contrast to other approaches that either can only predict a single time-step or require intensive offline training, this work used a computationally inexpensive method for learning and has the potential of providing temporally extended sets of predictions in real-time. Comparing the number of required manual switches between the machine-learned switching list and the best possible static lists showed an average decrease of 42.44% in the required switches for the machine-learned adaptive strategy. These promising results will facilitate the path for real-time application of this technique.

Impedance Variation and Learning Strategies in Human-Robot Interaction.

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.