Evaluating the Human-Robot Interaction (HRI) during tele-operation

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The aim of this PhDis to study Human Robot Interactions (HRI's) and assess the operators' mental workload and effort during HRI tasks and teleoperation (See Fig. 1 for the teleoperation setup). Beyond traditional approaches of using surveys to capture this data, this project will focus on using participants' brain and physiological data using the functional Near Infrared Spectroscopy (fNIRS) Brain Computer Interface. fNIRS is an emerging neuroimaging technique that offers a non-invasive, portable and low-cost method of monitoring brain activity (See Fig. 2 ). fNIRS is based on the use of near infrared spectroscopy to measure brain activity, and has been used to evaluate various tasks, including remotely operating vehicles [5], mental arithmetic [6], n-back tasks [5, 7], and other complex cognition tasks such as video games [4, 5, 8].Tele-operating a robot to perform a task remotely is still the only possible solution in many applications. When designing robots for the safety-critical, high-consequence environments domain (e.g. nuclear or surgery), autonomous robotics methods are not yet sufficiently trusted by highly conservative industries, which demand a human in the loop. Therefore, it is reasonable to assume that the interaction and interface created for the operators are of an acceptable/reduced work load by safely sharing the control between autonomous/intelligent system and huma operator. It is critical that the operator has the spare capacity to be able to respond in the case of the unexpected events. We will exploit different shared control techniques developed at the University of Lincoln in the past/ongoing teleoperation projects (e.g. using haptic-guidance and/or Verbally Sharing Control of robotic manipulators[1-3]). The aim of this work is to integrate novel evaluation techniques used to assess operators' mental workload and effort based on physiological and brain data, in the area of HRI and teleoperation, to complement existing techniques. Tele-operating a robotic manipulator, called slave-arm (SA) imposes a high cognitive load and mental workload even on expert human operators and, consequently, results in severe fatigue and progressive degeneration in performance [1-3].Current techniques of capturing operators' mental workload involve asking them to subjectively assess and self-report their levels of workloads using questionnaires such as NASA-TLX and Instantaneous Self-Assessment (ISA). The subjective measures become highly important when it comes to evaluating more complex systems and tasks, where performance-based measures become highly difficult to capture [9]. Even though they are critical for evaluation of these systems, there are certain limitations that cannot be overlooked when using them. Firstly, subjective measures rely on the participants' ability to judge and report the state throughout the task. This requires not only extra effort from the operator [10], but also skill and potential training. Secondly, subjective measures, if used in real-time have the potential to interrupt and negatively affect performance; if used post-task, they rely on the operators' ability to recall what happened during certain moments in the past. Direct physiological measures of brain activity using fNIRS (Fig. 2) offer the opportunity to capture and assess operators' mental workload whilst overcoming the above-mentioned limitations with subjective techniques and performance metrics. However, new research is needed in order to understand the considerations when applying physiological techniques during HRI [9].