12,659 research outputs found
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
Developing an intelligent vehicle which can perform human-like actions
requires the ability to learn basic driving skills from a large amount of
naturalistic driving data. The algorithms will become efficient if we could
decompose the complex driving tasks into motion primitives which represent the
elementary compositions of driving skills. Therefore, the purpose of this paper
is to segment unlabeled trajectory data into a library of motion primitives. By
applying a probabilistic inference based on an iterative
Expectation-Maximization algorithm, our method segments the collected
trajectories while learning a set of motion primitives represented by the
dynamic movement primitives. The proposed method utilizes the mutual
dependencies between the segmentation and representation of motion primitives
and the driving-specific based initial segmentation. By utilizing this mutual
dependency and the initial condition, this paper presents how we can enhance
the performance of both the segmentation and the motion primitive library
establishment. We also evaluate the applicability of the primitive
representation method to imitation learning and motion planning algorithms. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology intelligent vehicle platform. The results show
that the proposed approach can find the proper segmentation and establish the
motion primitive library simultaneously
Probabilistic segmentation applied to an assembly task
Movement primitives are a well established approach
for encoding and executing robot movements. While
the primitives themselves have been extensively researched, the
concept of movement primitive libraries has not received as
much attention. Libraries of movement primitives represent
the skill set of an agent and can be queried and sequenced in
order to solve specific tasks. The goal of this work is to segment
unlabeled demonstrations into an optimal set of skills. Our
novel approach segments the demonstrations while learning
a probabilistic representation of movement primitives. The
method differs from current approaches by taking advantage of
the often neglected, mutual dependencies between the segments
contained in the demonstrations and the primitives to be encoded.
Therefore, improving the combined quality of both segmentation
and skill learning. Furthermore, our method allows
incorporating domain specific insights using heuristics, which
are subsequently evaluated and assessed through probabilistic
inference methods. We demonstrate our method on a real robot
application, where the robot segments demonstrations of a chair
assembly task into a skill library. The library is subsequently
used to assemble the chair in an order not present in the
demonstrations
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
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives
We present a new parallel algorithm for probabilistic graphical model
optimization. The algorithm relies on data-parallel primitives (DPPs), which
provide portable performance over hardware architecture. We evaluate results on
CPUs and GPUs for an image segmentation problem. Compared to a serial baseline,
we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare
our performance to a reference, OpenMP-based algorithm, and find speedups of up
to 7X (CPU).Comment: LDAV 2018, October 201
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