Results 1 - 10 of 66

Analytic Moment-based Gaussian Process Filtering

by Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck
"... We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in ..."
Abstract - Cited by 36 (13 self) - Add to MetaCart
We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007). 1.

Learning GP-BayesFilters via Gaussian process latent variable models

by Jonathan Ko, Dieter Fox - In Proceedings of robotics: science and systems (RSS , 2009
"... Abstract — GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GP-BayesFilters learn nonparametric filter models from training data contain ..."
Abstract - Cited by 26 (4 self) - Add to MetaCart
Abstract — GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GP-BayesFilters learn nonparametric filter models from training data containing sequences of control inputs, observations, and ground truth states. The need for ground truth states limits the applicability of GP-BayesFilters to systems for which the ground truth can be estimated without prohibitive overhead. In this paper we introduce GPBF-LEARN, a framework for training GP-BayesFilters without any ground truth states. Our approach extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems. We show how weak labels for the ground truth states can be incorporated into the GPBF-LEARN framework. The approach is evaluated using a difficult tracking task, namely tracking a slotcar based on IMU measurements only. I.
(Show Context)

State-Space Inference and Learning with Gaussian Processes

by Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen
"... State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm ..."
Abstract - Cited by 21 (7 self) - Add to MetaCart
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Inference (filtering and smoothing) in linear dynamical systems (LDS) and nonlinear dynamical systems (NLDS) is frequently used in many areas, such as signal processing, state estimation, control, and finance/econometric models. Inference aims to estimate the state of a system from a stream of noisy measurements. Imagine tracking the location of a car based on odometer and GPS sensors, both of which are noisy. Sequential measurements from both sensors are combined to overcome the noise in the system and to obtain an accurate estimate of the system state. Even when the full state is only partially measured, it can still be inferred; in the car example the engine temperature is unobserved, but can be inferred via the nonlinear relationship from acceleration. To exploit this relationship appropriately, inference techniques in nonlinear models are required; they play an important role in many practical applications. LDS and NLDS belong to a class of models known as state-space models. A state-space model assumes that there exists a time sequence of latent states xt that evolve over time according to a Markovian process specified by a transition function f. The latent states are observed indirectly in y t through a measurement
(Show Context)

A bayesian nonparametric approach to modeling motion patterns,” Autonomous Robots, 2011, search and Pursuit/Evasion with Mobile Robots

by Joshua Joseph, Finale Doshi-velez, Nicholas Roy
"... Abstract — The batteries of many consumer products, includ-ing robots, are often both a substantial portion of the product’s cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery d ..."
Abstract - Cited by 19 (5 self) - Add to MetaCart
Abstract — The batteries of many consumer products, includ-ing robots, are often both a substantial portion of the product’s cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery dynamics are extremely complex, and we often lack the domain knowledge required to construct a model by hand. In this work, we take a data-driven approach and aim to learn a model of battery time-to-death from training data. Using a Dirichlet process prior over mixture weights, we learn an infinite mixture model for battery health. The Bayesian aspect of our model helps to avoid over-fitting while the nonparametric nature of the model allows the data to control the size of the model, preventing under-fitting. We demonstrate our model’s effectiveness by making time-to-death predictions using real data from nickel-metal hydride battery packs. I.

Variational gaussian process dynamical systems

by Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence - In Advances in Neural Information Processing System 25. IEEE Conf. publications , 2011
"... High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this pape ..."
Abstract - Cited by 17 (6 self) - Add to MetaCart
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the variational Gaussian process dynamical system. Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space. The approach also allows for the appropriate dimensionality of the latent space to be automatically determined. We demonstrate the model on a human motion capture data set and a series of high resolution video sequences. 1
(Show Context)

How to construct deep recurrent neural networks

by Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio , 2014
"... In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find th ..."
Abstract - Cited by 17 (3 self) - Add to MetaCart
In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN (Schmidhu-ber, 1992; El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conven-tional, shallow RNNs.

Model Learning for Robot Control: A Survey

by Duy Nguyen-tuong, Jan Peters - COGNITIVE SCIENCE
"... Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms.

by Marc Peter Deisenroth , Henrik Ohlsson - In Proceedings of the 2011 American Control Conference (ACC), , 2011
"... Abstract-We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This i ..."
Abstract - Cited by 13 (8 self) - Add to MetaCart
Abstract-We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing these moments. Based on this insight, we derive the cubature Kalman smoother and propose a novel robust filtering and smoothing algorithm based on Gibbs sampling.
(Show Context)

Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction

by Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters, Technische Universität Darmstadt
"... Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from ..."
Abstract - Cited by 12 (4 self) - Add to MetaCart
Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes. 1
(Show Context)

Probabilistic Modeling of Human Movements for Intention Inference

by Zhikun Wang, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters - In Proceedings of Robotics: Science and Systems (R:SS). 99 , 2012
"... Abstract—Inference of human intention may be an essential step towards understanding human actions and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intenti ..."
Abstract - Cited by 11 (3 self) - Add to MetaCart
Abstract—Inference of human intention may be an essential step towards understanding human actions and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human movements/actions. We introduce an efficient approximate inference algorithm to infer the human’s intention from an ongoing movement. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification. I.
(Show Context)