668 research outputs found
Model-free Probabilistic Movement Primitives for physical interaction
Physical interaction in robotics is a complex problem
that requires not only accurate reproduction of the kinematic
trajectories but also of the forces and torques exhibited
during the movement. We base our approach on Movement
Primitives (MP), as MPs provide a framework for modelling
complex movements and introduce useful operations on the
movements, such as generalization to novel situations, time
scaling, and others. Usually, MPs are trained with imitation
learning, where an expert demonstrates the trajectories. However,
MPs used in physical interaction either require additional
learning approaches, e.g., reinforcement learning, or are based
on handcrafted solutions. Our goal is to learn and generate
movements for physical interaction that are learned with imitation
learning, from a small set of demonstrated trajectories.
The Probabilistic Movement Primitives (ProMPs) framework
is a recent MP approach that introduces beneficial properties,
such as combination and blending of MPs, and represents the
correlations present in the movement. The ProMPs provides
a variable stiffness controller that reproduces the movement
but it requires a dynamics model of the system. Learning such
a model is not a trivial task, and, therefore, we introduce the
model-free ProMPs, that are learning jointly the movement and
the necessary actions from a few demonstrations. We derive
a variable stiffness controller analytically. We further extent
the ProMPs to include force and torque signals, necessary for
physical interaction. We evaluate our approach in simulated
and real robot tasks
Robust policy updates for stochastic optimal control
For controlling high-dimensional robots, most stochastic optimal control algorithms use approximations of the system dynamics and of the cost function (e.g., using linearizations and Taylor expansions). These approximations are typically only locally correct, which might cause instabilities in the greedy policy updates, lead to oscillations or the algorithms diverge. To overcome these drawbacks, we add a regularization term to the cost function that punishes large policy update steps in the trajectory optimization procedure. We applied this concept to the Approximate Inference Control method (AICO), where the resulting algorithm guarantees convergence for uninformative initial solutions without complex hand-tuning of learning rates. We evaluated our new algorithm on two simulated robotic platforms. A robot arm with five joints was used for reaching multiple targets while keeping the roll angle constant. On the humanoid robot Nao, we show how complex skills like reaching and balancing can be inferred from desired center of gravity or end effector coordinates
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net
Extracting low-dimensional control variables for movement primitives
Movement primitives (MPs) provide a powerful framework for data driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adapt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is pre-coded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for an intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset
Learning soft task priorities for control of redundant robots
Movement primitives (MPs) provide a powerful
framework for data driven movement generation that has been
successfully applied for learning from demonstrations and robot
reinforcement learning. In robotics we often want to solve a
multitude of different, but related tasks. As the parameters
of the primitives are typically high dimensional, a common
practice for the generalization of movement primitives to new
tasks is to adapt only a small set of control variables, also
called meta parameters, of the primitive. Yet, for most MP
representations, the encoding of these control variables is precoded
in the representation and can not be adapted to the
considered tasks. In this paper, we want to learn the encoding of
task-specific control variables also from data instead of relying
on fixed meta-parameter representations. We use hierarchical
Bayesian models (HBMs) to estimate a low dimensional latent
variable model for probabilistic movement primitives (ProMPs),
which is a recent movement primitive representation. We show
on two real robot datasets that ProMPs based on HBMs
outperform standard ProMPs in terms of generalization and
learning from a small amount of data and also allows for an
intuitive analysis of the movement. We also extend our HBM by
a mixture model, such that we can model different movement
types in the same dataset
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Reverse classification accuracy: predicting segmentation performance in the absence of ground truth
When integrating computational tools such as au- tomatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic
segmentation. However, the various proposed networks perform differently, with
behaviour largely influenced by architectural choices and training settings.
This paper explores Ensembles of Multiple Models and Architectures (EMMA) for
robust performance through aggregation of predictions from a wide range of
methods. The approach reduces the influence of the meta-parameters of
individual models and the risk of overfitting the configuration to a particular
database. EMMA can be seen as an unbiased, generic deep learning model which is
shown to yield excellent performance, winning the first position in the BRATS
2017 competition among 50+ participating teams.Comment: The method won the 1st-place in the Brain Tumour Segmentation (BRATS)
2017 competition (segmentation task
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Morphological analysis of longitudinal MR images plays a key role in
monitoring disease progression for prostate cancer patients, who are placed
under an active surveillance program. In this paper, we describe a
learning-based image registration algorithm to quantify changes on regions of
interest between a pair of images from the same patient, acquired at two
different time points. Combining intensity-based similarity and gland
segmentation as weak supervision, the population-data-trained registration
networks significantly lowered the target registration errors (TREs) on holdout
patient data, compared with those before registration and those from an
iterative registration algorithm. Furthermore, this work provides a
quantitative analysis on several longitudinal-data-sampling strategies and, in
turn, we propose a novel regularisation method based on maximum mean
discrepancy, between differently-sampled training image pairs. Based on 216 3D
MR images from 86 patients, we report a mean TRE of 5.6 mm and show
statistically significant differences between the different training data
sampling strategies.Comment: Accepted at MICCAI 202
- …