Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments

Bayesian Learning Experiments with a Khepera Robot

. This paper presents a new robotic programming environment based on the probability calculus. We show how reactive behaviours, like obstacle avoidance, contour following, or even light following, can be programmed and learned by the Khepera with our system. We further demonstrate that behaviours can be combined either by programmation or learning. A homing behaviour is thus obtained by combining obstacle avoidance and light following. 1 Introduction We propose a new robotic programming environment, which was tested on a Khepera robot. This system is based on the probability calculus. The choice of probabilities as a formal system allows an easy and rigorous translation of intuitive knowledge into a program. An example is the expression of dependence or independence between variables. In order to program a behaviour, the programmer will first have to state such a priori knowledge about the task at hand. This "seed" of program can then be tuned by confronting it to experimental data wh..

A bayesian framework for robotic programming

Abstract. We propose an original method for programming robots based on bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physicist E T Jaynes: “Probability as Logic”[1]. Learning and maximum entropy principle translate incompleteness into uncertainty. Bayesian inference offers a formal framework for reasoning with this uncertainty. The main contribution of this paper is the definition of a generic system of robotic programming and its experimental application. We illustrate it by programming a surveillance task with a mobile robot: the Khepera. In order to do this, we use generic programming resources called “descriptions”. We show how to define and use these resources in an incremental way (reactive behaviors, sensor fusion, situation recognition and sequences of behaviors) within a systematic and unified framework

Teaching Bayesian Behaviours to Video Game Characters

This article explores an application of Bayesian programming to behaviours for synthetic video games characters. We address the problem of real-time reactive selection of elementary behaviours for an agent playing a first person shooter game. We show how Bayesian programming can lead to condensed and easier formalisation of finite state machine-like behaviour selection, and lend itself to learning by imitation, in a fully transparent way for the player

Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/BSBTCD04/ address: Dagstuhl (DE) editor: Iida, F. and Pfeifer, R. and Steels, L. and Kuniyoshi, Y. publisher: Springer-VerlagCognition and Reasoning with uncertain and partial knowledge is prob- ably the biggest challenge for autonomous mobile robotics. Previous robotics sys- tems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a ma jor challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and method- ology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. This original formalism will be exemplified by two interesting experiments where robots are driven by a Bayesian Program (BP). These examples represents situations where the robot can sense only a small part of its global environment using noisy sensors. The second fact about these environments is they cannot be constrained so that to ease the control of the robot