Implications For Human-robot Interaction Research Articles

Multi-modal referring expressions in human-human task descriptions and their implications for human-robot interaction

AbstractHuman instructors often refer to objects and actions involved in a task description using both linguistic and non-linguistic means of communication. Hence, for robots to engage in natural human-robot interactions, we need to better understand the various relevant aspects of human multi-modal task descriptions. We analyse reference resolution to objects in a data collection comprising two object manipulation tasks (22 teacher student interactions in Task 1 and 16 in Task 2) and find that 78.76% of all referring expressions to the objects relevant in Task 1 are verbally underspecified and 88.64% of all referring expressions are verbally underspecified in Task 2. The data strongly suggests that a language processing module for robots must be genuinely multi-modal, allowing for seamless integration of information transmitted in the verbal and the visual channel, whereby tracking the speaker’s eye gaze and gestures as well as object recognition are necessary preconditions.

Covert Robot-Robot Communication: Human Perceptions and Implications for HRI

As future human-robot teams are envisioned for a variety of application domains, researchers have begun to investigate how humans and robots can communicate effectively and naturally in the context of human-robot team tasks. While a growing body of work is focused on human-robot communication and human perceptions thereof, there is currently little work on human perceptions of robot-robot communication. Understanding how robots should communicate information to each other in the presence of human teammates is an important open question for human-robot teaming. In this paper, we present two human-robot interaction (HRI) experiments investigating the human perception of verbal and silent robot-robot communication as part of a human-robot team task. The results suggest that silent communication of task-dependent, human-understandable information among robots is perceived as creepy by cooperative, co-located human teammates. Hence, we propose that, absent specific evidence to the contrary, robots in cooperative human-robot team settings need to be sensitive to human expectations about overt communication, and we encourage future work to investigate possible ways to modulate such expectations.

The Uncanny Valley [From the Field

More than 40 years ago, Masahiro Mori, a robotics professor at the Tokyo Institute of Technology, wrote an essay [1] on how he envisioned people's reactions to robots that looked and acted almost like a human. In particular, he hypothesized that a person's response to a humanlike robot would abruptly shift from empathy to revulsion as it approached, but failed to attain, a lifelike appearance. This descent into eeriness is known as the uncanny valley. The essay appeared in an obscure Japanese journal called Energy in 1970, and in subsequent years, it received almost no attention. However, more recently, the concept of the uncanny valley has rapidly attracted interest in robotics and other scientific circles as well as in popular culture. Some researchers have explored its implications for human-robot interaction and computer-graphics animation, whereas others have investigated its biological and social roots. Now interest in the uncanny valley should only intensify, as technology evolves and researchers build robots that look human. Although copies of Mori's essay have circulated among researchers, a complete version hasn't been widely available. The following is the first publication of an English translation that has been authorized and reviewed by Mori. (See “Turning Point” in this issue for an interview with Mori.).

Mutual Gaze: Implications for Human-Robot Interaction

Mutual Gaze: Implications for Human-Robot Interaction

The Tradeoff of Frame Rate and Resolution in a Route Clearing Task: Implications for Human-Robot Interaction

The implications of bandwidth allocation are described for teleoperation in a military task that involved navigation, target detection, and target identification. Color versus grayscale imagery was manipulated. Participants themselves traded off resolution and frame rate settings. Participants minimized switching between resolution/frame rate settings and tended to use settings with high resolution/low frame rate. Courses completed with the highest resolution (and lowest frame rate) had the fastest target identification times, but no other differences were observed between settings. Color imagery offered advantages for overall course time and the time to identify a tank as friendly or enemy.

The Tradeoff of Frame Rate and Resolution in a Route Clearing Task: Implications for Human-Robot Interaction

The implications of bandwidth allocation are described for teleoperation in a military task that involved navigation, target detection, and target identification. Color versus grayscale imagery was manipulated. Participants themselves traded off resolution and frame rate settings. Participants minimized switching between resolution/frame rate settings and tended to use settings with high resolution/low frame rate. Courses completed with the highest resolution (and lowest frame rate) had the fastest target identification times, but no other differences were observed between settings. Color imagery offered advantages for overall course time and the time to identify a tank as friendly or enemy.

Naturally occurring gestures in a human–robot teaching scenario

This paper describes our general framework for the investigation of how human gestures can be used to facilitate the interaction and communication between humans and robots. Two studies were carried out to reveal which “naturally occurring” gestures can be observed in a scenario where users had to explain to a robot how to perform a home task. Both studies followed a within-subjects design: participants had to demonstrate how to lay a table to a robot using two different methods — utilizing only gestures or gestures and speech. The first study enabled the validation of the COGNIRON coding scheme for human gestures in Human–Robot Interaction (HRI). Based on the data collected in both studies, an annotated video corpus was produced and characteristics such as frequency and duration of the different gestural classes have been gathered to help capture requirements for the designers of HRI systems. The results from the first study regarding the frequencies of the gestural types suggest an interaction between the order of presentation of the two methods and the actual type of gestures produced. However, the analysis of the speech produced along with the gestures did not reveal differences due to ordering of the experimental conditions. The second study expands the issues addressed by the first study: we aimed at extending the role of the interaction partner (the robot) by introducing some positive acknowledgement of the participants’ activity. The results show no significant differences in the distribution of gestures (frequency and duration) between the two explanation methods, in contrast to the previous study. Implications for HRI are discussed focusing on issues relevant for the design of the robot’s communication skills to support the interaction loop with humans in home scenarios.