On the subjective difficulty of Joystick-based robot arm teleoperation with auditory feedback (original) (raw)
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—Joystick-based teleoperation is a dominant method for remotely controlling various types of robots, such as excavators , cranes, and space telerobotics. Our ultimate goal is to create effective methods for training and assessing human operators of joystick-controlled robots. Towards that goal, an extensive study consisting of a total of 38 experimental subjects on both simulated as well as a physical robot, using either no feedback or auditory feedback, has been performed. In this paper, we present the complete experimental setup and we report only on the 18 experimental subjects teleoperating the simulated robot. Multiple observables were recorded, including not only joystick and robot angles and timings, but also subjective measures of difficulty, personality and usability data, and automated analysis of facial expressions and blink rate of the subjects. Our initial results indicate that: First, that the subjective difficulty of teleoperation with auditory feedback has smaller variance as compared to teleoperation without feedback. Second, that the subjective difficulty of a task is linearly related with the logarithm of task completion time. Third, we introduce two important indicators of operator performance, namely the Average Velocity of Robot Joints (AVRJ), and the Correct-to-Wrong-Joystick Direction Ratio (CWJR), and we show how these relate to accumulated user experience and with task time. We conclude with a forward-looking discussion including future steps.
Springer International Journal of Social Robotics, November 2012, Volume 4 (1S), pp. 5-18
Although tele-operation has a long history, when it comes to tuning, comparison, and evaluation of tele-operation systems, no standard framework exists which can fulfill desiderata such as: concisely modeling multiple aspects of the system as a whole, i.e. timing, accuracy, and event transitions, while also providing for separation of user-, feedback-, as well as learning-dependent components. On the other hand, real-time remote tele-operation of robotic arms, either industrial or humanoid, is highly suitable for a number of applications, especially in difficult or inaccessible environment, and thus such an evaluation framework would be desirable. Usually, teleoperation is driven by buttons, joysticks, haptic controllers, or slave-arms, providing an interface which can be quite cumbersome and unnatural, especially when operating robots with multiple degrees of freedom. Thus, in thus paper, we present a two-fold contribution: (a) a task-based teleoperation evaluation framework which can achieve the desiderata described above, as well as (b) a system for teleoperation of an industrial arm commanded through human-arm motion capture, which is used as a case study, and also serves to illustrate the effectiveness of the evaluation framework that we are introducing. In our system the desired trajectory of a remote robotic arm is easily and naturally controlled through imitation of simple movements of the operator’s physical arm, obtained through motion capture. Furthermore, an extensive real-world evaluation is provided, based on our proposed probabilistic framework, which contains an inter-subject quantitative study with 23 subjects, a longitudinal study with 6 subjects, as well as opinions and attitudes towards tele-operation study. The results provided illustrate the strengths of the proposed evaluation framework—by enabling the quick production of multiple task-, user-, system-, as well as learning-centric results, as well as the benefits of our natural imitation-based approach towards teleoperation. Furthermore, an interesting ordering of preferences towards different potential application areas of teleoperation is indicated by our data. Finally, after illustrating their effectiveness, we discuss how both our evaluation framework as well as teleoperation system presented are not only applicable in a wide variety of teleoperation domains, but are also directly extensible in many beneficial ways.
Multimodal Feedback in Human-Robot Interaction
Handbook of Research on Human-Computer Interfaces, Developments, and Applications
A major area of interest within the fields of human-computer interaction (HCI) and human-robot interaction (HRI) is user feedback. Previous work in HCI has investigated the effects of error feedback on task efficiency and error rates, yet, these studies have been mostly restricted to comparisons of inherently different feedback modalities, for example auditory and visual, and as such fail to acknowledge the many possible variations within each of these modalities, some of which being more effective than others. This chapter applies a user-centered approach to investigating feedback modalities for robot teleoperation by naïve users. It identifies the reasons why novice users need feedback when demonstrating novel behaviors to a teleoperated industrial robot and evaluates both various feedback modalities designed to prevent errors and, drawing on document design theory, studies different kinds of visual presentation regarding their effectiveness in the creation of legible error feedba...
2018
The continuing advancement in telerobotics is garnering increasing interest for space applications. Telerobotics enables the operator to interact with distant and harsh environments not reachable by most humans today. Depending on the suitability to the task, robots may be employed as an avatar (e.g. physical extension of the user), or a coworker to be supervised by the operator. This paper examines these different concepts through the lens of two space telerobotic missions: KONTUR-2, and METERON SUPVIS Justin. As a joint mission of German Aerospace Center (DLR) and Roscosmos, KONTUR-2 aims to study the effectiveness of force-feedback telepresence. A two degreesof-freedom force reflection joystick was deployed to the International Space Station (ISS) to allow the astronauts to command, among others, DLR's humanoid robot Space Justin, to perform different dexterous tasks including grasping of objects, and haptically interacting with a person on Earth. Commanding the robot through...