1,792 research outputs found
Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture
Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots
inside deep veins, which may block blood flow or even cause a life-threatening
pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by
pressing the target vein until its lumen is fully compressed. However, the
compression exam is highly operator-dependent. To alleviate intra- and
inter-variations, we present a robotic US system with a novel hybrid force
motion control scheme ensuring position and force tracking accuracy, and soft
landing of the probe onto the target surface. In addition, a path-based virtual
fixture is proposed to realize easy human-robot interaction for repeat
compression operation at the lesion location. To ensure the biometric
measurements obtained in different examinations are comparable, the 6D scanning
path is determined in a coarse-to-fine manner using both an external RGBD
camera and US images. The RGBD camera is first used to extract a rough scanning
path on the object. Then, the segmented vascular lumen from US images are used
to optimize the scanning path to ensure the visibility of the target object. To
generate a continuous scan path for developing virtual fixtures, an arc-length
based path fitting model considering both position and orientation is proposed.
Finally, the whole system is evaluated on a human-like arm phantom with an
uneven surface.Comment: Accepted Paper IEEE T-AS
Ongoing Tracking of Engagement in Motor Learning
Teaching motor skills such as playing music, handwriting, and driving, can
greatly benefit from recently developed technologies such as wearable gloves
for haptic feedback or robotic sensorimotor exoskeletons for the mediation of
effective human-human and robot-human physical interactions. At the heart of
such teacher-learner interactions still stands the critical role of the ongoing
feedback a teacher can get about the student's engagement state during the
learning and practice sessions. Particularly for motor learning, such feedback
is an essential functionality in a system that is developed to guide a teacher
on how to control the intensity of the physical interaction, and to best adapt
it to the gradually evolving performance of the learner. In this paper, our
focus is on the development of a near real-time machine-learning model that can
acquire its input from a set of readily available, noninvasive,
privacy-preserving, body-worn sensors, for the benefit of tracking the
engagement of the learner in the motor task. We used the specific case of
violin playing as a target domain in which data were empirically acquired, the
latent construct of engagement in motor learning was carefully developed for
data labeling, and a machine-learning model was rigorously trained and
validated
Quantifying Demonstration Quality for Robot Learning and Generalization
Learning from Demonstration (LfD) seeks to democratize robotics by enabling
diverse end-users to teach robots to perform a task by providing
demonstrations. However, most LfD techniques assume users provide optimal
demonstrations. This is not always the case in real applications where users
are likely to provide demonstrations of varying quality, that may change with
expertise and other factors. Demonstration quality plays a crucial role in
robot learning and generalization. Hence, it is important to quantify the
quality of the provided demonstrations before using them for robot learning. In
this paper, we propose quantifying the quality of the demonstrations based on
how well they perform in the learned task. We hypothesize that task performance
can give an indication of the generalization performance on similar tasks. The
proposed approach is validated in a user study (N = 27). Users with different
robotics expertise levels were recruited to teach a PR2 robot a generic task
(pressing a button) under different task constraints. They taught the robot in
two sessions on two different days to capture their teaching behaviour across
sessions. The task performance was utilized to classify the provided
demonstrations into high-quality and low-quality sets. The results show a
significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the
task performance and generalization performance across all participants. We
also found that users clustered into two groups: Users who provided
high-quality demonstrations from the first session, assigned to the
fast-adapters group, and users who provided low-quality demonstrations in the
first session and then improved with practice, assigned to the slow-adapters
group. These results highlight the importance of quantifying demonstration
quality, which can be indicative of the adaptation level of the user to the
task
Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot
Intelligent control of soft robots is challenging due to the nonlinear and
difficult-to-model dynamics. One promising model-free approach for soft robot
control is reinforcement learning (RL). However, model-free RL methods tend to
be computationally expensive and data-inefficient and may not yield natural and
smooth locomotion patterns for soft robots. In this work, we develop a
bio-inspired design of a learning-based goal-tracking controller for a soft
snake robot. The controller is composed of two modules: An RL module for
learning goal-tracking behaviors given the unmodeled and stochastic dynamics of
the robot, and a central pattern generator (CPG) with the Matsuoka oscillators
for generating stable and diverse locomotion patterns. We theoretically
investigate the maneuverability of Matsuoka CPG's oscillation bias, frequency,
and amplitude for steering control, velocity control, and sim-to-real
adaptation of the soft snake robot. Based on this analysis, we proposed a
composition of RL and CPG modules such that the RL module regulates the tonic
inputs to the CPG system given state feedback from the robot, and the output of
the CPG module is then transformed into pressure inputs to pneumatic actuators
of the soft snake robot. This design allows the RL agent to naturally learn to
entrain the desired locomotion patterns determined by the CPG maneuverability.
We validated the optimality and robustness of the control design in both
simulation and real experiments, and performed extensive comparisons with
state-of-art RL methods to demonstrate the benefit of our bio-inspired control
design.Comment: 20 pages, 17 figures, 4 tables, in IEEE Transactions on Robotic
Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems
As robotic systems are moved out of factory work cells into human-facing
environments questions of choreography become central to their design,
placement, and application. With a human viewer or counterpart present, a
system will automatically be interpreted within context, style of movement, and
form factor by human beings as animate elements of their environment. The
interpretation by this human counterpart is critical to the success of the
system's integration: knobs on the system need to make sense to a human
counterpart; an artificial agent should have a way of notifying a human
counterpart of a change in system state, possibly through motion profiles; and
the motion of a human counterpart may have important contextual clues for task
completion. Thus, professional choreographers, dance practitioners, and
movement analysts are critical to research in robotics. They have design
methods for movement that align with human audience perception, can identify
simplified features of movement for human-robot interaction goals, and have
detailed knowledge of the capacity of human movement. This article provides
approaches employed by one research lab, specific impacts on technical and
artistic projects within, and principles that may guide future such work. The
background section reports on choreography, somatic perspectives,
improvisation, the Laban/Bartenieff Movement System, and robotics. From this
context methods including embodied exercises, writing prompts, and community
building activities have been developed to facilitate interdisciplinary
research. The results of this work is presented as an overview of a smattering
of projects in areas like high-level motion planning, software development for
rapid prototyping of movement, artistic output, and user studies that help
understand how people interpret movement. Finally, guiding principles for other
groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for
the 21st Century)"
http://www.mdpi.com/journal/arts/special_issues/Machine_Artis
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