3,473 research outputs found
Spatial references with gaze and pointing in shared space of humans and robots
Renner P, Pfeiffer T, Wachsmuth I. Spatial references with gaze and pointing in shared space of humans and robots. In: Freksa C, Nebel B, Hegarty M, Barkowsky T, eds. Spatial Cognition IX. Lecture Notes in Computer Science. Vol 8684. Berlin [u.a.]: Springer; 2014: 121-136.For solving tasks cooperatively in close interaction with humans, robots need to have timely updated spatial representations. However, perceptual information about the current position of interaction partners is often late. If robots could anticipate the targets of upcoming manual actions, such as pointing gestures, they would have more time to physically react to human movements and could consider prospective space allocations in their planning. Many findings support a close eye-hand coordination in humans which could be used to predict gestures by observing eye gaze. However, effects vary strongly with the context of the interaction. We collect evidence of eye-hand coordination in a natural route planning scenario in which two agents interact over a map on a table. In particular, we are interested if fixations can predict pointing targets and how target distances affect the interlocutor's pointing behavior. We present an automatic method combining marker tracking and 3D modeling that provides eye and gesture measurements in real-time
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
Picking up objects requested by a human user is a common task in human-robot
interaction. When multiple objects match the user's verbal description, the
robot needs to clarify which object the user is referring to before executing
the action. Previous research has focused on perceiving user's multimodal
behaviour to complement verbal commands or minimising the number of follow up
questions to reduce task time. In this paper, we propose a system for reference
disambiguation based on visualisation and compare three methods to disambiguate
natural language instructions. In a controlled experiment with a YuMi robot, we
investigated real-time augmentations of the workspace in three conditions --
mixed reality, augmented reality, and a monitor as the baseline -- using
objective measures such as time and accuracy, and subjective measures like
engagement, immersion, and display interference. Significant differences were
found in accuracy and engagement between the conditions, but no differences
were found in task time. Despite the higher error rates in the mixed reality
condition, participants found that modality more engaging than the other two,
but overall showed preference for the augmented reality condition over the
monitor and mixed reality conditions
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Reasoning about space for human-robot interaction
L'interaction Homme-Robot est un domaine de recherche qui se développe de manière exponentielle durant ces dernières années, ceci nous procure de nouveaux défis au raisonnement géométrique du robot et au partage d'espace. Le robot pour accomplir une tâche, doit non seulement raisonner sur ses propres capacités, mais également prendre en considération la perception humaine, c'est à dire "Le robot doit se placer du point de vue de l'humain".
Chez l'homme, la capacité de prise de perspective visuelle commence à se manifester à partir du 24ème mois. Cette capacité est utilisée pour déterminer si une autre personne peut voir un objet ou pas. La mise en place de ce genre de capacités sociales améliorera les capacités cognitives du robot et aidera le robot pour une meilleure interaction avec les hommes.
Dans ce travail, nous présentons un mécanisme de raisonnement spatial de point de vue géométrique qui utilise des concepts psychologiques de la "prise de perspective" et "de la rotation mentale" dans deux cadres généraux:
- La planification de mouvement pour l'interaction homme-robot: le robot utilise "la prise de perspective égocentrique" pour évaluer plusieurs configurations où le robot peut effectuer différentes tâches d'interaction.
- Une interaction face à face entre l'homme et le robot : le robot emploie la prise de point de vue de l'humain comme un outil géométrique pour comprendre l'attention et l'intention humaine afin d'effectuer des tâches coopératives.Human Robot Interaction is a research area that is growing exponentially in last years. This fact brings new challenges to the robot's geometric reasoning and space sharing abilities. The robot should not only reason on its own capacities but also consider the actual situation by looking from human's eyes, thus "putting itself into human's perspective".
In humans, the "visual perspective taking" ability begins to appear by 24 months of age and is used to determine if another person can see an object or not. The implementation of this kind of social abilities will improve the robot's cognitive capabilities and will help the robot to perform a better interaction with human beings.
In this work, we present a geometric spatial reasoning mechanism that employs psychological concepts of "perspective taking" and "mental rotation" in two general frameworks:
- Motion planning for human-robot interaction: where the robot uses "egocentric perspective taking" to evaluate several configurations where the robot is able to perform different tasks of interaction.
- A face-to-face human-robot interaction: where the robot uses perspective taking of the human as a geometric tool to understand the human attention and intention in order to perform cooperative tasks
How to Communicate Robot Motion Intent: A Scoping Review
Robots are becoming increasingly omnipresent in our daily lives, supporting
us and carrying out autonomous tasks. In Human-Robot Interaction, human actors
benefit from understanding the robot's motion intent to avoid task failures and
foster collaboration. Finding effective ways to communicate this intent to
users has recently received increased research interest. However, no common
language has been established to systematize robot motion intent. This work
presents a scoping review aimed at unifying existing knowledge. Based on our
analysis, we present an intent communication model that depicts the
relationship between robot and human through different intent dimensions
(intent type, intent information, intent location). We discuss these different
intent dimensions and their interrelationships with different kinds of robots
and human roles. Throughout our analysis, we classify the existing research
literature along our intent communication model, allowing us to identify key
patterns and possible directions for future research.Comment: Interactive Data Visualization of the Paper Corpus:
https://rmi.robot-research.d
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