1,520 research outputs found
Extending Cobot's Motion Intention Visualization by Haptic Feedback
Nowadays, robots are found in a growing number of areas where they
collaborate closely with humans. Enabled by lightweight materials and safety
sensors, these cobots are gaining increasing popularity in domestic care,
supporting people with physical impairments in their everyday lives. However,
when cobots perform actions autonomously, it remains challenging for human
collaborators to understand and predict their behavior, which is crucial for
achieving trust and user acceptance. One significant aspect of predicting cobot
behavior is understanding their motion intention and comprehending how they
"think" about their actions. Moreover, other information sources often occupy
human visual and audio modalities, rendering them frequently unsuitable for
transmitting such information. We work on a solution that communicates cobot
intention via haptic feedback to tackle this challenge. In our concept, we map
planned motions of the cobot to different haptic patterns to extend the visual
intention feedback.Comment: Final CHI LBW 2023 submission:
https://dx.doi.org/10.1145/3544549.358560
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
Visualizing Robot Intent for Object Handovers with Augmented Reality
Humans are very skillful in communicating their intent for when and where a
handover would occur. On the other hand, even the state-of-the-art robotic
implementations for handovers display a general lack of communication skills.
We propose visualizing the internal state and intent of robots for
Human-to-Robot Handovers using Augmented Reality. Specifically, we visualize 3D
models of the object and the robotic gripper to communicate the robot's
estimation of where the object is and the pose that the robot intends to grasp
the object. We conduct a user study with 16 participants, in which each
participant handed over a cube-shaped object to the robot 12 times. Results
show that visualizing robot intent using augmented reality substantially
improves the subjective experience of the users for handovers and decreases the
time to transfer the object. Results also indicate that the benefits of
augmented reality are still present even when the robot makes errors in
localizing the object.Comment: 6 pages, 4 Figures, 2 Table
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
RICO-MR: An Open-Source Architecture for Robot Intent Communication through Mixed Reality
This article presents an open-source architecture for conveying robots'
intentions to human teammates using Mixed Reality and Head-Mounted Displays.
The architecture has been developed focusing on its modularity and re-usability
aspects. Both binaries and source code are available, enabling researchers and
companies to adopt the proposed architecture as a standalone solution or to
integrate it in more comprehensive implementations. Due to its scalability, the
proposed architecture can be easily employed to develop shared Mixed Reality
experiences involving multiple robots and human teammates in complex
collaborative scenarios.Comment: 6 pages, 3 figures, accepted for publication in the proceedings of
the 32nd IEEE International Conference on Robot and Human Interactive
Communication (RO-MAN
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