51,354 research outputs found
Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
Object manipulation actions represent an important share of the Activities of
Daily Living (ADLs). In this work, we study how to enable service robots to use
human multi-modal data to understand object manipulation actions, and how they
can recognize such actions when humans perform them during human-robot
collaboration tasks. The multi-modal data in this study consists of videos,
hand motion data, applied forces as represented by the pressure patterns on the
hand, and measurements of the bending of the fingers, collected as human
subjects performed manipulation actions. We investigate two different
approaches. In the first one, we show that multi-modal signal (motion, finger
bending and hand pressure) generated by the action can be decomposed into a set
of primitives that can be seen as its building blocks. These primitives are
used to define 24 multi-modal primitive features. The primitive features can in
turn be used as an abstract representation of the multi-modal signal and
employed for action recognition. In the latter approach, the visual features
are extracted from the data using a pre-trained image classification deep
convolutional neural network. The visual features are subsequently used to
train the classifier. We also investigate whether adding data from other
modalities produces a statistically significant improvement in the classifier
performance. We show that both approaches produce a comparable performance.
This implies that image-based methods can successfully recognize human actions
during human-robot collaboration. On the other hand, in order to provide
training data for the robot so it can learn how to perform object manipulation
actions, multi-modal data provides a better alternative
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Variation in early life maternal care predicts later long range frontal cortex synapse development in mice.
Empirical and theoretical work suggests that early postnatal experience may inform later developing synaptic connectivity to adapt the brain to its environment. We hypothesized that early maternal experience may program the development of synaptic density on long range frontal cortex projections. To test this idea, we used maternal separation (MS) to generate environmental variability and examined how MS affected 1) maternal care and 2) synapse density on virally-labeled long range axons of offspring reared in MS or control conditions. We found that MS and variation in maternal care predicted bouton density on dorsal frontal cortex axons that terminated in the basolateral amygdala (BLA) and dorsomedial striatum (DMS) with more, fragmented care associated with higher density. The effects of maternal care on these distinct axonal projections of the frontal cortex were manifest at different ages. Maternal care measures were correlated with frontal cortex → BLA bouton density at mid-adolescence postnatal (P) day 35 and frontal cortex → DMS bouton density in adulthood (P85). Meanwhile, we found no evidence that MS or maternal care affected bouton density on ascending orbitofrontal cortex (OFC) or BLA axons that terminated in the dorsal frontal cortices. Our data show that variation in early experience can alter development in a circuit-specific and age-dependent manner that may be relevant to understanding the effects of early life adversity
Robot Composite Learning and the Nunchaku Flipping Challenge
Advanced motor skills are essential for robots to physically coexist with
humans. Much research on robot dynamics and control has achieved success on
hyper robot motor capabilities, but mostly through heavily case-specific
engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous
manner, robot learning from human demonstration (LfD) has achieved great
progress, but still has limitations handling dynamic skills and compound
actions. In this paper, we present a composite learning scheme which goes
beyond LfD and integrates robot learning from human definition, demonstration,
and evaluation. The method tackles advanced motor skills that require dynamic
time-critical maneuver, complex contact control, and handling partly soft
partly rigid objects. We also introduce the "nunchaku flipping challenge", an
extreme test that puts hard requirements to all these three aspects. Continued
from our previous presentations, this paper introduces the latest update of the
composite learning scheme and the physical success of the nunchaku flipping
challenge
Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier
As universities recognize the inherent value in the data they collect and
hold, they encounter unforeseen challenges in stewarding those data in ways
that balance accountability, transparency, and protection of privacy, academic
freedom, and intellectual property. Two parallel developments in academic data
collection are converging: (1) open access requirements, whereby researchers
must provide access to their data as a condition of obtaining grant funding or
publishing results in journals; and (2) the vast accumulation of 'grey data'
about individuals in their daily activities of research, teaching, learning,
services, and administration. The boundaries between research and grey data are
blurring, making it more difficult to assess the risks and responsibilities
associated with any data collection. Many sets of data, both research and grey,
fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities
are exploiting these data for research, learning analytics, faculty evaluation,
strategic decisions, and other sensitive matters. Commercial entities are
besieging universities with requests for access to data or for partnerships to
mine them. The privacy frontier facing research universities spans open access
practices, uses and misuses of data, public records requests, cyber risk, and
curating data for privacy protection. This paper explores the competing values
inherent in data stewardship and makes recommendations for practice, drawing on
the pioneering work of the University of California in privacy and information
security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201
Multiform Adaptive Robot Skill Learning from Humans
Object manipulation is a basic element in everyday human lives. Robotic
manipulation has progressed from maneuvering single-rigid-body objects with
firm grasping to maneuvering soft objects and handling contact-rich actions.
Meanwhile, technologies such as robot learning from demonstration have enabled
humans to intuitively train robots. This paper discusses a new level of robotic
learning-based manipulation. In contrast to the single form of learning from
demonstration, we propose a multiform learning approach that integrates
additional forms of skill acquisition, including adaptive learning from
definition and evaluation. Moreover, going beyond state-of-the-art technologies
of handling purely rigid or soft objects in a pseudo-static manner, our work
allows robots to learn to handle partly rigid partly soft objects with
time-critical skills and sophisticated contact control. Such capability of
robotic manipulation offers a variety of new possibilities in human-robot
interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC),
Tysons Corner, VA, October 11-1
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics
Research (ISRR) 201
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