16,373 research outputs found
Multi-Robot Transfer Learning: A Dynamical System Perspective
Multi-robot transfer learning allows a robot to use data generated by a
second, similar robot to improve its own behavior. The potential advantages are
reducing the time of training and the unavoidable risks that exist during the
training phase. Transfer learning algorithms aim to find an optimal transfer
map between different robots. In this paper, we investigate, through a
theoretical study of single-input single-output (SISO) systems, the properties
of such optimal transfer maps. We first show that the optimal transfer learning
map is, in general, a dynamic system. The main contribution of the paper is to
provide an algorithm for determining the properties of this optimal dynamic map
including its order and regressors (i.e., the variables it depends on). The
proposed algorithm does not require detailed knowledge of the robots' dynamics,
but relies on basic system properties easily obtainable through simple
experimental tests. We validate the proposed algorithm experimentally through
an example of transfer learning between two different quadrotor platforms.
Experimental results show that an optimal dynamic map, with correct properties
obtained from our proposed algorithm, achieves 60-70% reduction of transfer
learning error compared to the cases when the data is directly transferred or
transferred using an optimal static map.Comment: 7 pages, 6 figures, accepted at the 2017 IEEE/RSJ International
Conference on Intelligent Robots and System
Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series
The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.National Science Foundation (IIS 0308213, IIS 0329009, CNS 0202067
Adaptive, locally-linear models of complex dynamics
The dynamics of complex systems generally include high-dimensional,
non-stationary and non-linear behavior, all of which pose fundamental
challenges to quantitative understanding. To address these difficulties we
detail a new approach based on local linear models within windows determined
adaptively from the data. While the dynamics within each window are simple,
consisting of exponential decay, growth and oscillations, the collection of
local parameters across all windows provides a principled characterization of
the full time series. To explore the resulting model space, we develop a novel
likelihood-based hierarchical clustering and we examine the eigenvalues of the
linear dynamics. We demonstrate our analysis with the Lorenz system undergoing
stable spiral dynamics and in the standard chaotic regime. Applied to the
posture dynamics of the nematode our approach identifies
fine-grained behavioral states and model dynamics which fluctuate close to an
instability boundary, and we detail a bifurcation in a transition from forward
to backward crawling. Finally, we analyze whole-brain imaging in
and show that the stability of global brain states changes with oxygen
concentration.Comment: 25 pages, 16 figure
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
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