Temporal Awareness in Teleoperation of Conversational Robots

Awareness of time is particularly important for teleoperation of conversational robots, both for controlling the robot and for estimating interaction success, because people have a low tolerance for long pauses in conversation. Findings have shown that people engaged in high-workload tasks tend to underestimate the passage of time. This study confirms that this problem exists for operators controlling a conversational robot, and it investigates mechanisms for improving temporal awareness and task performance while minimizing workload. In a laboratory experiment, two approaches to helping an operator perform various information input tasks were compared: first, assisting temporal awareness by using a clock display, and second, using autonomy to assist one of the operator's time-dependent tasks. Results revealed that assisting the task itself, even without the clock, improved not only task performance but also the operator's temporal awareness. However, results regarding the effect of the clock were ambiguous: it increased workload in general and did not help temporal awareness overall, but it did improve temporal awareness for text entry tasks in particular. As text entry is an important task for teleoperation of social robots, we further investigated the problem of improving temporal awareness during text entry tasks. As the first experiment suggested the effectiveness of a clock, we further validated that the clock is specifically useful to improve temporal awareness. These results showed that the clock did not increase workload for text entry tasks; however, for touch-typing operators, the results suggested that showing a clock after the end of an interaction, rather than continuously throughout the task, could lower the operator's perceived workload.

[1]  Michael A. Goodrich,et al.  Comparing the usefulness of video and map information in navigation tasks , 2006, HRI '06.

[2]  Takashi Minato,et al.  Generating natural motion in an android by mapping human motion , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  C Fortin,et al.  Temporal interval production and processing in working memory , 1995, Perception & psychophysics.

[4]  Laurel D. Riek,et al.  A decomposition of UAV-related situation awareness , 2006, HRI '06.

[5]  E. Schegloff,et al.  A simplest systematics for the organization of turn-taking for conversation , 1974 .

[6]  Reid G. Simmons,et al.  Attaining situational awareness for sliding autonomy , 2006, HRI '06.

[7]  R. Rousseau,et al.  Time estimation and concurrent nontemporal processing: Specific interference from short-term-memory demands , 1993, Perception & psychophysics.

[8]  R. Rousseau,et al.  Time estimation as an index of processing demand in memory search , 1987, Perception & psychophysics.

[9]  M. Yamamoto,et al.  Timing control effects of utterance to communicative actions on embodied interaction with a robot , 2004, RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759).

[10]  Takayuki Kanda,et al.  Field trial for simultaneous teleoperation of mobile social robots , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[11]  Kristinn R. Thórisson,et al.  Natural Turn-Taking Needs No Manual: Computational Theory and Model, from Perception to Action , 2002 .

[12]  S. Feldstein,et al.  Rhythms of dialogue , 1970 .

[13]  Takayuki Kanda,et al.  How Quickly Should a Communication Robot Respond? Delaying Strategies and Habituation Effects , 2009, Int. J. Soc. Robotics.

[14]  Takayuki Kanda,et al.  Simultaneous teleoperation of multiple social robots , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[15]  Jean Scholtz,et al.  Common metrics for human-robot interaction , 2006, HRI '06.

[16]  Michael A. Goodrich,et al.  Ecological Interfaces for Improving Mobile Robot Teleoperation , 2007, IEEE Transactions on Robotics.

[17]  Margaret McLaughlin,et al.  Conversation: How Talk Is Organized , 1984 .

[18]  Jeffrey D. Anderson,et al.  Managing autonomy in robot teams: Observations from four experiments , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[19]  Julie A. Adams,et al.  Compass visualizations for human-robotic interaction , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[20]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[21]  Nikolaos Mavridis,et al.  Steps towards Affordable Android Telepresence , 2011 .

[22]  Chrystopher L. Nehaniv,et al.  Behaviour delay and robot expressiveness in child-robot interactions: A user study on interaction kinesics , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[23]  Irwin Altman,et al.  Temporal aspects of computer-mediated communication , 1988 .

[24]  Brian F. Goldiez,et al.  A Paradigm Shift in Interactive Computing: Deriving Multimodal Design Principles from Behavioral and Neurological Foundations , 2004, Int. J. Hum. Comput. Interact..

[25]  John Anderson,et al.  An integrated theory of prospective time interval estimation: the role of cognition, attention, and learning. , 2007, Psychological review.

[26]  Michael A. Goodrich,et al.  On using mixed-initiative control: A perspective for managing large-scale robotic teams , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[27]  P. Fraisse.  Perception and estimation of time. , 1984, Annual review of psychology.

[28]  François Michaud,et al.  Egocentric and exocentric teleoperation interface using real-time, 3D video projection , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[29]  R. Miall,et al.  Distinct systems for automatic and cognitively controlled time measurement: evidence from neuroimaging , 2003, Current Opinion in Neurobiology.

[30]  Krzysztof Nowosielski,et al.  Controlling process performance indicators. Results of empirical and theoretical research , 2014 .

[31]  Nikolaos Mavridis,et al.  Real-time Teleoperation of an Industrial Robotic Arm Through Human Arm Movement Imitation , 2010 .

[32]  R. E. Hicks,et al.  Concurrent processing demands and the experience of time-in-passing , 1977 .

[33]  Takayuki Kanda,et al.  An affective guide robot in a shopping mall , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[34]  Takayuki Kanda,et al.  Providing route directions: Design of robot's utterance, gesture, and timing , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).