177,611 research outputs found
Mixtures of controlled Gaussian processes for dynamical modeling of deformable objects
Control and manipulation of objects is a highly relevant topic in Robotics research. Although significant advances have been made over the manipulation of rigid bodies, the manipulation of non-rigid objects is still challenging and an open problem. Due to the uncertainty of the outcome when applying physical actions to non-rigid objects, using prior knowledge on objects’ dynamics can greatly improve the control performance. However, fitting such models is a challenging task for materials such as clothing, where the state is represented by points in a mesh, resulting in very large dimensionality that makes models difficult to learn, process and predict based on measured data. In this paper, we expand previous work on Controlled Gaussian Process Dynamical Models (CGPDM), a method that uses a non-linear projection of the state space onto a much smaller dimensional latent space, and learns the object dynamics in the latent space. We take advantage of the variability in training data by employing Mixture of Experts (MoE), and we devise theory and experimental validations that demonstrate significant improvements in training and prediction times, plus robustness and error stability when predicting deformable objects exposed to disparate movement ranges.This work was partially developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”), which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930). We would like to thank the members of the HCRL Lab and the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin for their feedback during the development of this work.Peer ReviewedPostprint (published version
On the Geometry of Message Passing Algorithms for Gaussian Reciprocal Processes
Reciprocal processes are acausal generalizations of Markov processes
introduced by Bernstein in 1932. In the literature, a significant amount of
attention has been focused on developing dynamical models for reciprocal
processes. Recently, probabilistic graphical models for reciprocal processes
have been provided. This opens the way to the application of efficient
inference algorithms in the machine learning literature to solve the smoothing
problem for reciprocal processes. Such algorithms are known to converge if the
underlying graph is a tree. This is not the case for a reciprocal process,
whose associated graphical model is a single loop network. The contribution of
this paper is twofold. First, we introduce belief propagation for Gaussian
reciprocal processes. Second, we establish a link between convergence analysis
of belief propagation for Gaussian reciprocal processes and stability theory
for differentially positive systems.Comment: 15 pages; Typos corrected; This paper introduces belief propagation
for Gaussian reciprocal processes and extends the convergence analysis in
arXiv:1603.04419 to the Gaussian cas
Sequential Bayesian inference for static parameters in dynamic state space models
A method for sequential Bayesian inference of the static parameters of a
dynamic state space model is proposed. The method is based on the observation
that many dynamic state space models have a relatively small number of static
parameters (or hyper-parameters), so that in principle the posterior can be
computed and stored on a discrete grid of practical size which can be tracked
dynamically. Further to this, this approach is able to use any existing
methodology which computes the filtering and prediction distributions of the
state process. Kalman filter and its extensions to non-linear/non-Gaussian
situations have been used in this paper. This is illustrated using several
applications: linear Gaussian model, Binomial model, stochastic volatility
model and the extremely non-linear univariate non-stationary growth model.
Performance has been compared to both existing on-line method and off-line
methods
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