An operational model of joint attention - Timing of the initiate-act in interactions with a virtual human

Pfeiffer-Leßmann N, Pfeiffer T, Wachsmuth I. An operational model of joint attention - Timing of the initiate-act in interactions with a virtual human. In: Dörner D, Goebel R, Oaksford M, Pauen M, Stern E, eds. Proceedings of KogWis 2012. Bamberg, Germany: University of Bamberg Press; 2012: 96-97

3D gaze cursor: continuous calibration and end-point grasp control of robotic actuators

© 2016 IEEE.Eye movements are closely related to motor actions, and hence can be used to infer motor intentions. Additionally, eye movements are in some cases the only means of communication and interaction with the environment for paralysed and impaired patients with severe motor deficiencies. Despite this, eye-tracking technology still has a very limited use as a human-robot control interface and its applicability is highly restricted to 2D simple tasks that operate on screen based interfaces and do not suffice for natural physical interaction with the environment. We propose that decoding the gaze position in 3D space rather than in 2D results into a much richer spatial cursor signal that allows users to perform everyday tasks such as grasping and moving objects via gaze-based robotic teleoperation. Eye tracking in 3D calibration is usually slow - we demonstrate here that by using a full 3D trajectory for system calibration generated by a robotic arm rather than a simple grid of discrete points, gaze calibration in the 3 dimensions can be successfully achieved in short time and with high accuracy. We perform the non-linear regression from eye-image to 3D-end point using Gaussian Process regressors, which allows us to handle uncertainty in end-point estimates gracefully. Our telerobotic system uses a multi-joint robot arm with a gripper and is integrated with our in-house GT3D binocular eye tracker. This prototype system has been evaluated and assessed in a test environment with 7 users, yielding gaze-estimation errors of less than 1cm in the horizontal, vertical and depth dimensions, and less than 2cm in the overall 3D Euclidean space. Users reported intuitive, low-cognitive load, control of the system right from their first trial and were straightaway able to simply look at an object and command through a wink to grasp this object with the robot gripper

A brain-machine interface for assistive robotic control

Brain-machine interfaces (BMIs) are the only currently viable means of communication for many individuals suffering from locked-in syndrome (LIS) – profound paralysis that results in severely limited or total loss of voluntary motor control. By inferring user intent from task-modulated neurological signals and then translating those intentions into actions, BMIs can enable LIS patients increased autonomy. Significant effort has been devoted to developing BMIs over the last three decades, but only recently have the combined advances in hardware, software, and methodology provided a setting to realize the translation of this research from the lab into practical, real-world applications. Non-invasive methods, such as those based on the electroencephalogram (EEG), offer the only feasible solution for practical use at the moment, but suffer from limited communication rates and susceptibility to environmental noise. Maximization of the efficacy of each decoded intention, therefore, is critical. This thesis addresses the challenge of implementing a BMI intended for practical use with a focus on an autonomous assistive robot application. First an adaptive EEG- based BMI strategy is developed that relies upon code-modulated visual evoked potentials (c-VEPs) to infer user intent. As voluntary gaze control is typically not available to LIS patients, c-VEP decoding methods under both gaze-dependent and gaze- independent scenarios are explored. Adaptive decoding strategies in both offline and online task conditions are evaluated, and a novel approach to assess ongoing online BMI performance is introduced. Next, an adaptive neural network-based system for assistive robot control is presented that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. Exploratory learning, or “learning by doing,” is an unsupervised method in which the robot is able to build an internal model for motor planning and coordination based on real-time sensory inputs received during exploration. Finally, a software platform intended for practical BMI application use is developed and evaluated. Using online c-VEP methods, users control a simple 2D cursor control game, a basic augmentative and alternative communication tool, and an assistive robot, both manually and via high-level goal-oriented commands