32,865 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
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
A study of event traffic during the shared manipulation of objects within a collaborative virtual environment
Event management must balance consistency and responsiveness above the requirements of shared object interaction within a Collaborative Virtual Environment
(CVE) system. An understanding of the event traffic during collaborative tasks helps in the design of all aspects of a CVE system. The application, user activity, the display
interface, and the network resources, all play a part in determining the characteristics of event management.
Linked cubic displays lend themselves well to supporting natural social human communication between remote users. To allow users to communicate naturally and subconsciously, continuous and detailed tracking is necessary. This, however, is hard to balance with the real-time consistency constraints of general shared object interaction.
This paper aims to explain these issues through a detailed examination of event traffic produced by a typical CVE, using both immersive and desktop displays, while supporting a variety of collaborative activities. We analyze event traffic during a highly collaborative task requiring various forms of shared object manipulation, including the concurrent manipulation of a shared object. Event sources are categorized and the influence of the form of object sharing as well as the display device
interface are detailed. With the presented findings the paper wishes to aid the design of future systems
Dexterous Manipulation Graphs
We propose the Dexterous Manipulation Graph as a tool to address in-hand
manipulation and reposition an object inside a robot's end-effector. This graph
is used to plan a sequence of manipulation primitives so to bring the object to
the desired end pose. This sequence of primitives is translated into motions of
the robot to move the object held by the end-effector. We use a dual arm robot
with parallel grippers to test our method on a real system and show successful
planning and execution of in-hand manipulation
Compound droplet manipulations on fiber arrays
Recent works demonstrated that fiber arrays may constitue the basis of an
open digital microfluidics. Various processes, such as droplet motion,
fragmentation, trapping, release, mixing and encapsulation, may be achieved on
fiber arrays. However, handling a large number of tiny droplets resulting from
the mixing of several liquid components is still a challenge for developing
microreactors, smart sensors or microemulsifying drugs. Here, we show that the
manipulation of tiny droplets onto fiber networks allows for creating compound
droplets with a high complexity level. Moreover, this cost-effective and
flexible method may also be implemented with optical fibers in order to develop
fluorescence-based biosensor
Next generation space robot
The recent research effort on the next generation space robots is presented. The goals of this research are to develop the fundamental technologies and to acquire the design parameters of the next generation space robot. Visual sensing and perception, dexterous manipulation, man machine interface and artificial intelligence techniques such as task planning are identified as the key technologies
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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