33 research outputs found
Adaptive robot body learning and estimation through predictive coding
The predictive functions that permit humans to infer their body state by
sensorimotor integration are critical to perform safe interaction in complex
environments. These functions are adaptive and robust to non-linear actuators
and noisy sensory information. This paper introduces a computational perceptual
model based on predictive processing that enables any multisensory robot to
learn, infer and update its body configuration when using arbitrary sensors
with Gaussian additive noise. The proposed method integrates different sources
of information (tactile, visual and proprioceptive) to drive the robot belief
to its current body configuration. The motivation is to enable robots with the
embodied perception needed for self-calibration and safe physical human-robot
interaction.
We formulate body learning as obtaining the forward model that encodes the
sensor values depending on the body variables, and we solve it by Gaussian
process regression. We model body estimation as minimizing the discrepancy
between the robot body configuration belief and the observed posterior. We
minimize the variational free energy using the sensory prediction errors
(sensed vs expected).
In order to evaluate the model we test it on a real multisensory robotic arm.
We show how different sensor modalities contributions, included as additive
errors, improve the refinement of the body estimation and how the system adapts
itself to provide the most plausible solution even when injecting strong
sensory visuo-tactile perturbations. We further analyse the reliability of the
model when different sensor modalities are disabled. This provides grounded
evidence about the correctness of the perceptual model and shows how the robot
estimates and adjusts its body configuration just by means of sensory
information.Comment: Accepted for IEEE International Conference on Intelligent Robots and
Systems (IROS 2018
Drifting perceptual patterns suggest prediction errors fusion rather than hypothesis selection: replicating the rubber-hand illusion on a robot
Humans can experience fake body parts as theirs just by simple visuo-tactile
synchronous stimulation. This body-illusion is accompanied by a drift in the
perception of the real limb towards the fake limb, suggesting an update of body
estimation resulting from stimulation. This work compares body limb drifting
patterns of human participants, in a rubber hand illusion experiment, with the
end-effector estimation displacement of a multisensory robotic arm enabled with
predictive processing perception. Results show similar drifting patterns in
both human and robot experiments, and they also suggest that the perceptual
drift is due to prediction error fusion, rather than hypothesis selection. We
present body inference through prediction error minimization as one single
process that unites predictive coding and causal inference and that it is
responsible for the effects in perception when we are subjected to intermodal
sensory perturbations.Comment: Proceedings of the 2018 IEEE International Conference on Development
and Learning and Epigenetic Robotic
Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization
The accurate estimation of the noise covariance matrix (NCM) in a dynamic
system is critical for state estimation and control, as it has a major
influence in their optimality. Although a large number of NCM estimation
methods have been developed, most of them assume the noises to be white.
However, in many real-world applications, the noises are colored (e.g., they
exhibit temporal autocorrelations), resulting in suboptimal solutions. Here, we
introduce a novel brain-inspired algorithm that accurately and adaptively
estimates the NCM for dynamic systems subjected to colored noise. Particularly,
we extend the Dynamic Expectation Maximization algorithm to perform both online
noise covariance and state estimation by optimizing the free energy objective.
We mathematically prove that our NCM estimator converges to the global optimum
of this free energy objective. Using randomized numerical simulations, we show
that our estimator outperforms nine baseline methods with minimal noise
covariance estimation error under colored noise conditions. Notably, we show
that our method outperforms the best baseline (Variational Bayes) in joint
noise and state estimation for high colored noise. We foresee that the accuracy
and the adaptive nature of our estimator make it suitable for online estimation
in real-world applications.Comment: 62nd IEEE Conference on Decision and Contro
Minimum time search of moving targets in uncertain environments
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 19-07-2013Esta tesis aborda el desarrollo de un sistema autónomo para buscar un objetivo móvil en el menor tiempo posible sobre un entorno con incertidumbre, es decir, para resolver el problema de búsqueda de tiempo mínimo, que se presenta como un problema especial dentro de la teoría de búsqueda óptima. Se propone una solución Bayesiana para encontrar el objetivo utilizando varios agentes móviles con dinámica restringida provistos de sensores que proporcionan información del entorno. La búsqueda de tiempo mínimo involucra dos procesos: la estimación de la ubicación del objetivo a partir de la información recogida por los agentes que cooperan en la búsqueda, y el diseño de la planificación de las rutas que deben seguir los agentes para encontrar el objetivo. La estimación de la ubicación del objetivo se aborda utilizando técnicas Bayesianas, más específicamente, el filtro recursivo Bayesiano. Además, se propone un filtro de información, basado en el filtro de Kalman extendido, que afronta el problema de los retrasos en la comunicación (problema de medidas desordenadas). La planificación de las trayectorias de los agentes se plantea como un problema de decisión secuencial donde, a partir de la estimación de la ubicación del objetivo, se calculan las mejores acciones que los agentes tienen que realizar. Para ello se proponen tres estrategias Bayesianas: minimización del tiempo local de detección esperado, maximización de la probabilidad de detección descontada por una función dependiente del tiempo, y optimización de una función probabilística que integra una heurística que aproxima la observación esperada. Para implementar las estrategias se proponen tres soluciones. La primera, basada en la programación con restricciones, ofrece soluciones exactas para el caso discreto cuando el objeto es estático y el número de variables de decisión pequeño. La segunda es un algoritmo aproximado construido a partir del método de optimización de entropía cruzada que aborda el caso discreto para objetos dinámicos. La tercera es un algoritmo descentralizado basado en el método del gradiente que calcula decisiones en un horizonte limitado, teniendo en cuenta el futuro, en el caso continuo. Los problemas de búsqueda de tiempo mínimo se encuentran en el planteamiento de muchas aplicaciones reales, como son las operaciones de emergencia de búsqueda y rescate (p.e. rescate de náufragos en accidentes marítimos) o el control de la difusión de sustancias contaminantes (p.e. monitorización de derrames de petróleo). Esta tesis muestra cómo reducir el tiempo de búsqueda de un objeto móvil de forma eficiente, determinando qué estrategias de búsqueda tienen en cuenta el tiempo y bajo qué condiciones son válidas, y proporcionando algoritmos polinómicos que calculen las acciones que los agentes tienen que realizar para encontrar el objeto.This thesis is concerned with the development of an autonomous system to search a dynamic target in the minimum possible time in uncertain environments, that is, to solve the minimum time search problem, which is presented as an especial problem within the optimal search theory. This work proposes a Bayesian approach to nd the target using several moving agents with constrained dynamics and equipped with sensors that provide information about the environment. The minimum time search involves two process: the target location estimation using the information collected by the agents, and the planning of the searching routes that the agents must follow to nd the target. The target location estimation is tackled using Bayesian techniques, more precisely, the recursive Bayesian lter. Moreover, an improved information lter, based on the extended Kalman lter, that deals with the team communication delays (i.e. out of sequence problem) is presented. The agents trajectory planning is faced as a sequential decision making problem where, given the a priori target location estimation, the best actions that the agents have to perform are computed. For that purpose, three Bayesian strategies are proposed: minimizing the local expected time of detection, maximizing the discounted time probability of detection, and optimizing a probabilistic function that integrates an heuristic that approximates the expected observation. To implement the strategies, three solutions are proposed. The rst one, based on constraint programming, provides exact solutions in the discrete case when the target is static and the number of decision variables is small. The second one is an approximated algorithm stood on the cross entropy optimization method that tackles the discrete case for dynamic targets. The third solution is a gradient-based decentralized algorithm that achieves non-myopic solutions for the continuous case. The minimum time search problems are found inside the core of many real applications, such as search and rescue emergency operations (e.g. shipwreck accidents) or pollution substances di usion control (e.g. oil spill monitoring). This thesis reveals how to reduce the searching time of a moving target e ciently, determining which searching strategies take into account the time and under which conditions are valid, and providing approximated polynomial algorithms to compute the actions that the agents must perform to find the target.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu
Sistema de identificación y seguimiento de superficies geográficas basadas en UAV con visión artificial
Master en Investigación en Informática, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática , curso 2007-2008Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu
Sistema Software para el Robot Guía del Museo de Informática García Santesmases
El proyecto consiste en el desarrollo de un sistema para la
resolución del problema de la planificación de trayectorias en dos
dimensiones, para la navegación de robots móviles en superficies planas.
Este sistema será utilizado por el robot guía del museo García‐
Santesmases de la facultad de Informática de la Universidad Complutense
de Madrid. El robot tendrá la capacidad de reproducir sonidos con el
objetivo de explicar a los visitantes las distintas piezas del museo.
Asimismo se desarrollará una aplicación remota mediante
tecnología inalámbrica, que se conectará al robot para monitorizar su
posición y estado dentro del museo y para poder controlarlo
manualmente. Se creará un entorno de visualización en dos dimensiones,
que simule el comportamiento del robot en respuesta a la planificación
elaborada por nuestro sistema. El entorno estará compuesto por
obstáculos tanto fijos como aleatorios.
[ABSTRACT]
The project consists of the development of a system for solving the
problem of two dimensional path planning for the navigation of mobile
autonomous robots on flat surfaces. This system will be used by the robot
guide of the Garcia‐Santesmases museum in the Faculty of Computer
Sciences of the Complutense University of Madrid. The robot will be able
to reproduce sounds aimed at explaining the different equipment of the
museum to its visitors.
Likewise we will develop a remote application using wireless
technology, which will be connected to the robot in order to monitor its
position and state within the museum, and to enable it to be manually
controlled. A two dimensional visual enviroment will be created which
will simulate the behaviour of the robot in response to the planning made
by our system. The enviroment will be composed of different fixed as well
as random obstacles
End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
We present a pixel-based deep active inference algorithm (PixelAI) inspired
by human body perception and action. Our algorithm combines the free-energy
principle from neuroscience, rooted in variational inference, with deep
convolutional decoders to scale the algorithm to directly deal with raw visual
input and provide online adaptive inference. Our approach is validated by
studying body perception and action in a simulated and a real Nao robot.
Results show that our approach allows the robot to perform 1) dynamical body
estimation of its arm using only monocular camera images and 2) autonomous
reaching to "imagined" arm poses in the visual space. This suggests that robot
and human body perception and action can be efficiently solved by viewing both
as an active inference problem guided by ongoing sensory input
Closed-form control with spike coding networks
Efficient and robust control using spiking neural networks (SNNs) is still an
open problem. Whilst behaviour of biological agents is produced through sparse
and irregular spiking patterns, which provide both robust and efficient
control, the activity patterns in most artificial spiking neural networks used
for control are dense and regular -- resulting in potentially less efficient
codes. Additionally, for most existing control solutions network training or
optimization is necessary, even for fully identified systems, complicating
their implementation in on-chip low-power solutions. The neuroscience theory of
Spike Coding Networks (SCNs) offers a fully analytical solution for
implementing dynamical systems in recurrent spiking neural networks -- while
maintaining irregular, sparse, and robust spiking activity -- but it's not
clear how to directly apply it to control problems. Here, we extend SCN theory
by incorporating closed-form optimal estimation and control. The resulting
networks work as a spiking equivalent of a linear-quadratic-Gaussian
controller. We demonstrate robust spiking control of simulated
spring-mass-damper and cart-pole systems, in the face of several perturbations,
including input- and system-noise, system disturbances, and neural silencing.
As our approach does not need learning or optimization, it offers opportunities
for deploying fast and efficient task-specific on-chip spiking controllers with
biologically realistic activity.Comment: Under review in an IEEE journa