3,655 research outputs found
Generative Temporal Models with Spatial Memory for Partially Observed Environments
In model-based reinforcement learning, generative and temporal models of
environments can be leveraged to boost agent performance, either by tuning the
agent's representations during training or via use as part of an explicit
planning mechanism. However, their application in practice has been limited to
simplistic environments, due to the difficulty of training such models in
larger, potentially partially-observed and 3D environments. In this work we
introduce a novel action-conditioned generative model of such challenging
environments. The model features a non-parametric spatial memory system in
which we store learned, disentangled representations of the environment.
Low-dimensional spatial updates are computed using a state-space model that
makes use of knowledge on the prior dynamics of the moving agent, and
high-dimensional visual observations are modelled with a Variational
Auto-Encoder. The result is a scalable architecture capable of performing
coherent predictions over hundreds of time steps across a range of partially
observed 2D and 3D environments.Comment: ICML 201
Incrementally Learned Mixture Models for GNSS Localization
GNSS localization is an important part of today's autonomous systems,
although it suffers from non-Gaussian errors caused by non-line-of-sight
effects. Recent methods are able to mitigate these effects by including the
corresponding distributions in the sensor fusion algorithm. However, these
approaches require prior knowledge about the sensor's distribution, which is
often not available. We introduce a novel sensor fusion algorithm based on
variational Bayesian inference, that is able to approximate the true
distribution with a Gaussian mixture model and to learn its parametrization
online. The proposed Incremental Variational Mixture algorithm automatically
adapts the number of mixture components to the complexity of the measurement's
error distribution. We compare the proposed algorithm against current
state-of-the-art approaches using a collection of open access real world
datasets and demonstrate its superior localization accuracy.Comment: 8 pages, 5 figures, published in proceedings of IEEE Intelligent
Vehicles Symposium (IV) 201
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
Probably Unknown: Deep Inverse Sensor Modelling In Radar
Radar presents a promising alternative to lidar and vision in autonomous
vehicle applications, able to detect objects at long range under a variety of
weather conditions. However, distinguishing between occupied and free space
from raw radar power returns is challenging due to complex interactions between
sensor noise and occlusion.
To counter this we propose to learn an Inverse Sensor Model (ISM) converting
a raw radar scan to a grid map of occupancy probabilities using a deep neural
network. Our network is self-supervised using partial occupancy labels
generated by lidar, allowing a robot to learn about world occupancy from past
experience without human supervision. We evaluate our approach on five hours of
data recorded in a dynamic urban environment. By accounting for the scene
context of each grid cell our model is able to successfully segment the world
into occupied and free space, outperforming standard CFAR filtering approaches.
Additionally by incorporating heteroscedastic uncertainty into our model
formulation, we are able to quantify the variance in the uncertainty throughout
the sensor observation. Through this mechanism we are able to successfully
identify regions of space that are likely to be occluded.Comment: 6 full pages, 1 page of reference
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Despite its omnipresence in robotics application, the nature of spatial knowledgeand the mechanisms that underlie its emergence in autonomous agents are stillpoorly understood. Recent theoretical works suggest that the Euclidean structure ofspace induces invariants in an agent’s raw sensorimotor experience. We hypothesizethat capturing these invariants is beneficial for sensorimotor prediction and that,under certain exploratory conditions, a motor representation capturing the structureof the external space should emerge as a byproduct of learning to predict futuresensory experiences. We propose a simple sensorimotor predictive scheme, applyit to different agents and types of exploration, and evaluate the pertinence of thesehypotheses. We show that a naive agent can capture the topology and metricregularity of its sensor’s position in an egocentric spatial frame without any a prioriknowledge, nor extraneous supervision
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