1,018 research outputs found
Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration
In this paper, we present a motion-based robotic communication framework that
enables non-verbal communication among autonomous underwater vehicles (AUVs)
and human divers. We design a gestural language for AUV-to-AUV communication
which can be easily understood by divers observing the conversation unlike
typical radio frequency, light, or audio based AUV communication. To allow AUVs
to visually understand a gesture from another AUV, we propose a deep network
(RRCommNet) which exploits a self-attention mechanism to learn to recognize
each message by extracting maximally discriminative spatio-temporal features.
We train this network on diverse simulated and real-world data. Our
experimental evaluations, both in simulation and in closed-water robot trials,
demonstrate that the proposed RRCommNet architecture is able to decipher
gesture-based messages with an average accuracy of 88-94% on simulated data,
73-83% on real data (depending on the version of the model used). Further, by
performing a message transcription study with human participants, we also show
that the proposed language can be understood by humans, with an overall
transcription accuracy of 88%. Finally, we discuss the inference runtime of
RRCommNet on embedded GPU hardware, for real-time use on board AUVs in the
field
Hand and Arm Gesture-based Human-Robot Interaction: A Review
The study of Human-Robot Interaction (HRI) aims to create close and friendly
communication between humans and robots. In the human-center HRI, an essential
aspect of implementing a successful and effective HRI is building a natural and
intuitive interaction, including verbal and nonverbal. As a prevalent
nonverbally communication approach, hand and arm gesture communication happen
ubiquitously in our daily life. A considerable amount of work on gesture-based
HRI is scattered in various research domains. However, a systematic
understanding of the works on gesture-based HRI is still lacking. This paper
intends to provide a comprehensive review of gesture-based HRI and focus on the
advanced finding in this area. Following the stimulus-organism-response
framework, this review consists of: (i) Generation of human gesture(stimulus).
(ii) Robot recognition of human gesture(organism). (iii) Robot reaction to
human gesture(response). Besides, this review summarizes the research status of
each element in the framework and analyze the advantages and disadvantages of
related works. Toward the last part, this paper discusses the current research
challenges on gesture-based HRI and provides possible future directions.Comment: 10 pages, 1 figure
Diver Interest via Pointing in Three Dimensions: 3D Pointing Reconstruction for Diver-AUV Communication
This paper presents Diver Interest via Pointing in Three Dimensions (DIP-3D),
a method to relay an object of interest from a diver to an autonomous
underwater vehicle (AUV) by pointing that includes three-dimensional distance
information to discriminate between multiple objects in the AUV's camera image.
Traditional dense stereo vision for distance estimation underwater is
challenging because of the relative lack of saliency of scene features and
degraded lighting conditions. Yet, including distance information is necessary
for robotic perception of diver pointing when multiple objects appear within
the robot's image plane. We subvert the challenges of underwater distance
estimation by using sparse reconstruction of keypoints to perform pose
estimation on both the left and right images from the robot's stereo camera.
Triangulated pose keypoints, along with a classical object detection method,
enable DIP-3D to infer the location of an object of interest when multiple
objects are in the AUV's field of view. By allowing the scuba diver to point at
an arbitrary object of interest and enabling the AUV to autonomously decide
which object the diver is pointing to, this method will permit more natural
interaction between AUVs and human scuba divers in underwater-human robot
collaborative tasks.Comment: Under Review International Conference of Robotics and Automation 202
Challenges in Collaborative HRI for Remote Robot Teams
Collaboration between human supervisors and remote teams of robots is highly
challenging, particularly in high-stakes, distant, hazardous locations, such as
off-shore energy platforms. In order for these teams of robots to truly be
beneficial, they need to be trusted to operate autonomously, performing tasks
such as inspection and emergency response, thus reducing the number of
personnel placed in harm's way. As remote robots are generally trusted less
than robots in close-proximity, we present a solution to instil trust in the
operator through a `mediator robot' that can exhibit social skills, alongside
sophisticated visualisation techniques. In this position paper, we present
general challenges and then take a closer look at one challenge in particular,
discussing an initial study, which investigates the relationship between the
level of control the supervisor hands over to the mediator robot and how this
affects their trust. We show that the supervisor is more likely to have higher
trust overall if their initial experience involves handing over control of the
emergency situation to the robotic assistant. We discuss this result, here, as
well as other challenges and interaction techniques for human-robot
collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019
Workshop: The Challenges of Working on Social Robots that Collaborate with
People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing
Systems, May 2019, Glasgow, U
HREyes: Design, Development, and Evaluation of a Novel Method for AUVs to Communicate Information and Gaze Direction
We present the design, development, and evaluation of HREyes: biomimetic
communication devices which use light to communicate information and, for the
first time, gaze direction from AUVs to humans. First, we introduce two types
of information displays using the HREye devices: active lucemes and ocular
lucemes. Active lucemes communicate information explicitly through animations,
while ocular lucemes communicate gaze direction implicitly by mimicking human
eyes. We present a human study in which our system is compared to the use of an
embedded digital display that explicitly communicates information to a diver by
displaying text. Our results demonstrate accurate recognition of active lucemes
for trained interactants, limited intuitive understanding of these lucemes for
untrained interactants, and relatively accurate perception of gaze direction
for all interactants. The results on active luceme recognition demonstrate more
accurate recognition than previous light-based communication systems for AUVs
(albeit with different phrase sets). Additionally, the ocular lucemes we
introduce in this work represent the first method for communicating gaze
direction from an AUV, a critical aspect of nonverbal communication used in
collaborative work. With readily available hardware as well as open-source and
easily re-configurable programming, HREyes can be easily integrated into any
AUV with the physical space for the devices and used to communicate effectively
with divers in any underwater environment with appropriate visibility.Comment: Under submission at ICRA2
Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques
Underwater Gesture Recognition is a challenging task since conditions which are normally not an issue in gesture recognition on land must be considered. Such issues include low visibility, low contrast, and unequal spectral propagation. In this work, we explore the underwater gesture recognition problem by taking on the recently released Cognitive Autonomous Diving Buddy Underwater Gestures dataset. The contributions of this paper are as follows: (1) Use traditional computer vision techniques along with classical machine learning to perform gesture recognition on the CADDY dataset; (2) Apply deep learning using a convolutional neural network to solve the same problem; (3) Perform confusion matrix analysis to determine the types of gestures that are relatively difficult to recognize and understand why; (4) Compare the performance of the methods above in terms of accuracy and inference speed. We achieve up to 97.06% accuracy with our CNN. To the best of our knowledge, our work is one of the earliest attempts, if not the first, to apply computer vision and machine learning techniques for gesture recognition on the said dataset. As such, we hope this work will serve as a benchmark for future work on the CADDY dataset
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