4 research outputs found
Expressivity in Natural and Artificial Systems
Roboticists are trying to replicate animal behavior in artificial systems.
Yet, quantitative bounds on capacity of a moving platform (natural or
artificial) to express information in the environment are not known. This paper
presents a measure for the capacity of motion complexity -- the expressivity --
of articulated platforms (both natural and artificial) and shows that this
measure is stagnant and unexpectedly limited in extant robotic systems. This
analysis indicates trends in increasing capacity in both internal and external
complexity for natural systems while artificial, robotic systems have increased
significantly in the capacity of computational (internal) states but remained
more or less constant in mechanical (external) state capacity. This work
presents a way to analyze trends in animal behavior and shows that robots are
not capable of the same multi-faceted behavior in rich, dynamic environments as
natural systems.Comment: Rejected from Nature, after review and appeal, July 4, 2018
(submitted May 11, 2018
Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Optical marker-based motion capture is a vital tool in applications such as
motion and behavioural analysis, animation, and biomechanics. Labelling, that
is, assigning optical markers to the pre-defined positions on the body is a
time consuming and labour intensive postprocessing part of current motion
capture pipelines. The problem can be considered as a ranking process in which
markers shuffled by an unknown permutation matrix are sorted to recover the
correct order. In this paper, we present a framework for automatic marker
labelling which first estimates a permutation matrix for each individual frame
using a differentiable permutation learning model and then utilizes temporal
consistency to identify and correct remaining labelling errors. Experiments
conducted on the test data show the effectiveness of our framework
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