1,058 research outputs found
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as
a rich framework for real-world planning and control problems, especially in
robotics. However exact solutions in this framework are typically
computationally intractable for all but the smallest problems. A well-known
technique for speeding up POMDP solving involves performing value backups at
specific belief points, rather than over the entire belief simplex. The
efficiency of this approach, however, depends greatly on the selection of
points. This paper presents a set of novel techniques for selecting informative
belief points which work well in practice. The point selection procedure is
combined with point-based value backups to form an effective anytime POMDP
algorithm called Point-Based Value Iteration (PBVI). The first aim of this
paper is to introduce this algorithm and present a theoretical analysis
justifying the choice of belief selection technique. The second aim of this
paper is to provide a thorough empirical comparison between PBVI and other
state-of-the-art POMDP methods, in particular the Perseus algorithm, in an
effort to highlight their similarities and differences. Evaluation is performed
using both standard POMDP domains and realistic robotic tasks
Finding Approximate POMDP solutions Through Belief Compression
Standard value function approaches to finding policies for Partially
Observable Markov Decision Processes (POMDPs) are generally considered to be
intractable for large models. The intractability of these algorithms is to a
large extent a consequence of computing an exact, optimal policy over the
entire belief space. However, in real-world POMDP problems, computing the
optimal policy for the full belief space is often unnecessary for good control
even for problems with complicated policy classes. The beliefs experienced by
the controller often lie near a structured, low-dimensional subspace embedded
in the high-dimensional belief space. Finding a good approximation to the
optimal value function for only this subspace can be much easier than computing
the full value function. We introduce a new method for solving large-scale
POMDPs by reducing the dimensionality of the belief space. We use Exponential
family Principal Components Analysis (Collins, Dasgupta and Schapire, 2002) to
represent sparse, high-dimensional belief spaces using small sets of learned
features of the belief state. We then plan only in terms of the low-dimensional
belief features. By planning in this low-dimensional space, we can find
policies for POMDP models that are orders of magnitude larger than models that
can be handled by conventional techniques. We demonstrate the use of this
algorithm on a synthetic problem and on mobile robot navigation tasks
Memory Aware Synapses: Learning what (not) to forget
Humans can learn in a continuous manner. Old rarely utilized knowledge can be
overwritten by new incoming information while important, frequently used
knowledge is prevented from being erased. In artificial learning systems,
lifelong learning so far has focused mainly on accumulating knowledge over
tasks and overcoming catastrophic forgetting. In this paper, we argue that,
given the limited model capacity and the unlimited new information to be
learned, knowledge has to be preserved or erased selectively. Inspired by
neuroplasticity, we propose a novel approach for lifelong learning, coined
Memory Aware Synapses (MAS). It computes the importance of the parameters of a
neural network in an unsupervised and online manner. Given a new sample which
is fed to the network, MAS accumulates an importance measure for each parameter
of the network, based on how sensitive the predicted output function is to a
change in this parameter. When learning a new task, changes to important
parameters can then be penalized, effectively preventing important knowledge
related to previous tasks from being overwritten. Further, we show an
interesting connection between a local version of our method and Hebb's
rule,which is a model for the learning process in the brain. We test our method
on a sequence of object recognition tasks and on the challenging problem of
learning an embedding for predicting triplets.
We show state-of-the-art performance and, for the first time, the ability to
adapt the importance of the parameters based on unlabeled data towards what the
network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201
Evaluation of laser range-finder mapping for agricultural spraying vehicles
In this paper, we present a new application of laser range-finder sensing to agricultural spraying vehicles. The current generation of spraying vehicles use automatic controllers to maintain the height of the sprayer booms above the crop.
However, these control systems are typically based on ultrasonic sensors mounted on the booms, which limits the accuracy of the measurements and the response of the controller to changes in the terrain, resulting in a sub-optimal spraying process. To overcome these limitations, we propose to use a laser scanner, attached to the front of the sprayer's cabin, to scan the ground surface in front of the vehicle and to build a scrolling 3d map of the terrain. We evaluate the proposed solution in a series of field tests, demonstrating that the approach provides a more detailed and accurate representation of the environment than the current sonar-based solution, and which can lead to the development of more efficient boom control systems
Appearance-based localization for mobile robots using digital zoom and visual compass
This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally
Temporal Correlations and Persistence in the Kinetic Ising Model: the Role of Temperature
We study the statistical properties of the sum , that is the difference of time spent positive or negative by the
spin , located at a given site of a -dimensional Ising model
evolving under Glauber dynamics from a random initial configuration. We
investigate the distribution of and the first-passage statistics
(persistence) of this quantity. We discuss successively the three regimes of
high temperature (), criticality (), and low temperature
(). We discuss in particular the question of the temperature
dependence of the persistence exponent , as well as that of the
spectrum of exponents , in the low temperature phase. The
probability that the temporal mean was always larger than the
equilibrium magnetization is found to decay as . This
yields a numerical determination of the persistence exponent in the
whole low temperature phase, in two dimensions, and above the roughening
transition, in the low-temperature phase of the three-dimensional Ising model.Comment: 21 pages, 11 PostScript figures included (1 color figure
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Visual recognition algorithms are required today to exhibit adaptive
abilities. Given a deep model trained on a specific, given task, it would be
highly desirable to be able to adapt incrementally to new tasks, preserving
scalability as the number of new tasks increases, while at the same time
avoiding catastrophic forgetting issues. Recent work has shown that masking the
internal weights of a given original conv-net through learned binary variables
is a promising strategy. We build upon this intuition and take into account
more elaborated affine transformations of the convolutional weights that
include learned binary masks. We show that with our generalization it is
possible to achieve significantly higher levels of adaptation to new tasks,
enabling the approach to compete with fine tuning strategies by requiring
slightly more than 1 bit per network parameter per additional task. Experiments
on two popular benchmarks showcase the power of our approach, that achieves the
new state of the art on the Visual Decathlon Challenge
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers
This paper improves state-of-the-art visual object trackers that use online
adaptation. Our core contribution is an offline meta-learning-based method to
adjust the initial deep networks used in online adaptation-based tracking. The
meta learning is driven by the goal of deep networks that can quickly be
adapted to robustly model a particular target in future frames. Ideally the
resulting models focus on features that are useful for future frames, and avoid
overfitting to background clutter, small parts of the target, or noise. By
enforcing a small number of update iterations during meta-learning, the
resulting networks train significantly faster. We demonstrate this approach on
top of the high performance tracking approaches: tracking-by-detection based
MDNet and the correlation based CREST. Experimental results on standard
benchmarks, OTB2015 and VOT2016, show that our meta-learned versions of both
trackers improve speed, accuracy, and robustness.Comment: Code: https://github.com/silverbottlep/meta_tracker
Experimental analysis of sample-based maps for long-term SLAM
This paper presents a system for long-term SLAM (simultaneous localization and mapping) by mobile service robots and its experimental evaluation in a real dynamic environment. To deal with the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns), the environment is represented at multiple timescales simultaneously (5 in our experiments). A sample-based representation is
proposed, where older memories fade at different rates depending on the timescale, and robust statistics are used to interpret the samples. The dynamics of this representation are analysed in a five week experiment, measuring the relative influence of short- and long-term memories over time, and further demonstrating the robustness of the approach
Gestures Everywhere: A Multimodal Sensor Fusion and Analysis Framework for Pervasive Displays
Gestures Everywhere is a dynamic framework for multimodal sensor fusion, pervasive analytics and gesture recognition. Our framework aggregates the real-time data from approximately 100 sensors that include RFID readers, depth cameras and RGB cameras distributed across 30 interactive displays that are located in key public areas of the MIT Media Lab. Gestures Everywhere fuses the multimodal sensor data using radial basis function particle filters and performs real-time analysis on the aggregated data. This includes key spatio-temporal properties such as presence, location and identity; in addition to higher-level analysis including social clustering and gesture recognition. We describe the algorithms and architecture of our system and discuss the lessons learned from the systems deployment
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