An assistive decision-and-control architecture for force-sensitive hand–arm systems driven by human–machine interfaces

Fully autonomous applications of modern robotic systems are still constrained by limitations in sensory data processing, scene interpretation, and automated reasoning. However, their use as assistive devices for people with upper-limb disabilities has become possible with recent advances in “soft robotics”, that is, interaction control, physical human–robot interaction, and reflex planning. In this context, impedance and reflex-based control has generally been understood to be a promising approach to safe interaction robotics. To create semi-autonomous assistive devices, we propose a decision-and-control architecture for hand–arm systems with “soft robotics” capabilities, which can then be used via human–machine interfaces (HMIs). We validated the functionality of our approach within the BrainGate2 clinical trial, in which an individual with tetraplegia used our architecture to control a robotic hand–arm system under neural control via a multi-electrode array implanted in the motor cortex. The neuroscience results of this research have previously been published by Hochberg et al. In this paper we present our assistive decision-and-control architecture and demonstrate how the semi-autonomous assistive behavior can help the user. In our framework the robot is controlled through a multi-priority Cartesian impedance controller and its behavior is extended with collision detection and reflex reaction. Furthermore, virtual workspaces are added to ensure safety. On top of this we employ a decision-and-control architecture that uses sensory information available from the robotic system to evaluate the current state of task execution. Based on a set of available assistive skills, our architecture provides support in object interaction and manipulation and thereby enhances the usability of the robotic system for use with HMIs. The goal of our development is to provide an easy-to-use robotic system for people with physical disabilities and thereby enable them to perform simple tasks of daily living. In an exemplary real-world task, the participant was able to serve herself a beverage autonomously for the first time since her brainstem stroke, which she suffered approximately 14 years prior to this research