29,638 research outputs found
Relationship descriptors for interactive motion adaptation
In this thesis we present an interactive motion adaptation scheme for close
interactions between skeletal characters and mesh structures, such as navigating
restricted environments and manipulating tools.
We propose a new spatial-relationship based representation to encode
character-object interactions describing the kinematics of the body parts by the
weighted sum of vectors relative to descriptor points selectively sampled over the
scene. In contrast to previous discrete representations that either only handle
static spatial relationships, or require offline, costly optimization processes, our
continuous framework smoothly adapts the motion of a character to deformations
in the objects and character morphologies in real-time whilst preserving the
original context and style of the scene.
We demonstrate the strength of working in our relationship-descriptor
space in tackling the issue of motion editing under large environment
deformations by integrating procedural animation techniques such as
repositioning contacts in an interaction whilst preserving the context and style of
the original animation.
Furthermore we propose a method that can be used to adapt animations
from template objects to novel ones by solving for mappings between the two in
our relationship-descriptor space effectively transferring an entire motion from
one object to a new one of different geometry whilst ensuring continuity across
all frames of the animation, as opposed to mapping static poses only as is
traditionally achieved.
The experimental results show that our method can be used for a wide
range of applications, including motion retargeting for dynamically changing
scenes, multi-character interactions, and interactive character control and
deformation transfer for scenes that involve close interactions. We further
demonstrate a key use case in retargeting locomotion to uneven terrains and
curving paths convincingly for bipeds and quadrupeds.
Our framework is useful for artists who need to design animated scenes
interactively, and modern computer games that allow users to design their own
virtual characters, objects and environments, such that they can recycle existing
motion data for a large variety of different configurations without the need to
manually reconfigure motion from scratch or store expensive combinations of
animation in memory. Most importantly it’s achieved in real-time
Motor fluency shapes abstract concepts
People with different types of bodies tend to think differently in predictable ways, even about abstract ideas that seem far removed from bodily experience. Right- and left-handers implicitly associate positive ideas like goodness and honesty more strongly with their dominant side of space, the side on which they can interact with their environment more fluently, and negative ideas with their non-dominant side. This suggests a role for motor experience in shaping abstract thoughts. Yet, previous evidence is also consistent with an experience-independent account. Here we show that right-handers' tendency to associate 'good' with right and 'bad' with left can be reversed due to both long- and short-term changes in motor fluency. Among stroke patients who were right-handed prior to unilateral cerebrovascular accident (CVA), those with left-hemiparesis (following right CVA) associated good with right, but those with right-hemiparesis (following left CVA) associated good with left, like natural left-handers. A similar pattern was found in healthy right-handers whose right or left hand was temporarily handicapped in a laboratory training task. Motor experience influences judgments of good and bad, overriding any predispositions due to natural handedness. Even highly abstract ideas depend, in part, on how people interact with the physical world
Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates
In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures
Coyote\u27s Tale on the Old Oregon Trail: Challenging Cultural Memory through Narrative at the Tamástslikt Cultural Institute
This essay examines the oppositional narratives presented in a Native American museum in order to explore the efficacy of narrative as both a strategy for resistance to hegemonic narratives of the settling of the West and a medium for sharing culture. The positioning of the museum visitor as co-participant in the museum’s narratives is also considered, with a particular focus on the relationships among narrator, story, and audience. Finally, the narrative of tribal life presented in the museum is evaluated for its potential as a vehicle for both cultural change and continuity
Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models
Recent progress in human-robot collaboration makes fast and fluid
interactions possible, even when human observations are partial and occluded.
Methods like Interaction Probabilistic Movement Primitives (ProMP) model human
trajectories through motion capture systems. However, such representation does
not properly model tasks where similar motions handle different objects. Under
current approaches, a robot would not adapt its pose and dynamics for proper
handling. We integrate the use of Electromyography (EMG) into the Interaction
ProMP framework and utilize muscular signals to augment the human observation
representation. The contribution of our paper is increased task discernment
when trajectories are similar but tools are different and require the robot to
adjust its pose for proper handling. Interaction ProMPs are used with an
augmented vector that integrates muscle activity. Augmented time-normalized
trajectories are used in training to learn correlation parameters and robot
motions are predicted by finding the best weight combination and temporal
scaling for a task. Collaborative single task scenarios with similar motions
but different objects were used and compared. For one experiment only joint
angles were recorded, for the other EMG signals were additionally integrated.
Task recognition was computed for both tasks. Observation state vectors with
augmented EMG signals were able to completely identify differences across
tasks, while the baseline method failed every time. Integrating EMG signals
into collaborative tasks significantly increases the ability of the system to
recognize nuances in the tasks that are otherwise imperceptible, up to 74.6% in
our studies. Furthermore, the integration of EMG signals for collaboration also
opens the door to a wide class of human-robot physical interactions based on
haptic communication that has been largely unexploited in the field.Comment: 7 pages, 2 figures, 2 tables. As submitted to Humanoids 201
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
Degenerate Variational Integrators for Magnetic Field Line Flow and Guiding Center Trajectories
Symplectic integrators offer many advantages for the numerical solution of
Hamiltonian differential equations, including bounded energy error and the
preservation of invariant sets. Two of the central Hamiltonian systems
encountered in plasma physics --- the flow of magnetic field lines and the
guiding center motion of magnetized charged particles --- resist symplectic
integration by conventional means because the dynamics are most naturally
formulated in non-canonical coordinates, i.e., coordinates lacking the familiar
partitioning. Recent efforts made progress toward non-canonical
symplectic integration of these systems by appealing to the variational
integration framework; however, those integrators were multistep methods and
later found to be numerically unstable due to parasitic mode instabilities.
This work eliminates the multistep character and, therefore, the parasitic mode
instabilities via an adaptation of the variational integration formalism that
we deem ``degenerate variational integration''. Both the magnetic field line
and guiding center Lagrangians are degenerate in the sense that their resultant
Euler-Lagrange equations are systems of first-order ODEs. We show that
retaining the same degree of degeneracy when constructing a discrete Lagrangian
yields one-step variational integrators preserving a non-canonical symplectic
structure on the original Hamiltonian phase space. The advantages of the new
algorithms are demonstrated via numerical examples, demonstrating superior
stability compared to existing variational integrators for these systems and
superior qualitative behavior compared to non-conservative algorithms
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