Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manipulation tasks, like grasping and assembly, but it requires estimation of the state of contact between the robot arm and the objects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the state of contact. The current state of contact is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the measurement We show that their parameters can be estimated from measurements concurrently with the estimation of the parameters of the movement in each state of contact. The learning algorithm is a variant of the EM procedure. The E step is computed exactly; solving the M step exactly would require solving a set of coupled nonlinear algebraic equations in the parameters. Instead, gradient ascent is used to produce an increase in likelihood

Probabilistic Methodology and Techniques for Artefact Conception and Development

The purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism called Bayesian Programming. This formalism is then used to review the main probabilistic methodology and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art

Survey: Probabilistic Methodology and Techniques for Artefact Conception and Development

Projet CYBERMOVEThe purpose of this paper is to make a state of the art on probabilistic methodology and techniques for artefact conception and development. It is the 8th deliverable of the BIBA (Bayesian Inspired Brain and Artefacts) project. We first present the incompletness problem as the central difficulty that both living creatures and artefacts have to face: how can they perceive, infer, decide and act efficiently with incomplete and uncertain knowledge?. We then introduce a generic probabilistic formalism Called Bayesian Programming. This formalism is then used to review the main probabilistic methodology and techniques. This review is organized in 3 parts: first the probabilistic models from Bayesian networks to Kalman filters and from sensor fusion to CAD systems, second the inference techniques and finally the learning and model acquisition and comparison methodologies. We conclude with the perspectives of the BIBA project as they rise from this state of the art

Towards perceptual intelligence : statistical modeling of human individual and interactive behaviors

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. 279-297).This thesis presents a computational framework for the automatic recognition and prediction of different kinds of human behaviors from video cameras and other sensors, via perceptually intelligent systems that automatically sense and correctly classify human behaviors, by means of Machine Perception and Machine Learning techniques. In the thesis I develop the statistical machine learning algorithms (dynamic graphical models) necessary for detecting and recognizing individual and interactive behaviors. In the case of the interactions two Hidden Markov Models (HMMs) are coupled in a novel architecture called Coupled Hidden Markov Models (CHMMs) that explicitly captures the interactions between them. The algorithms for learning the parameters from data as well as for doing inference with those models are developed and described. Four systems that experimentally evaluate the proposed paradigm are presented: (1) LAFTER, an automatic face detection and tracking system with facial expression recognition; (2) a Tai-Chi gesture recognition system; (3) a pedestrian surveillance system that recognizes typical human to human interactions; (4) and a SmartCar for driver maneuver recognition. These systems capture human behaviors of different nature and increasing complexity: first, isolated, single-user facial expressions, then, two-hand gestures and human-to-human interactions, and finally complex behaviors where human performance is mediated by a machine, more specifically, a car. The metric that is used for quantifying the quality of the behavior models is their accuracy: how well they are able to recognize the behaviors on testing data. Statistical machine learning usually suffers from lack of data for estimating all the parameters in the models. In order to alleviate this problem, synthetically generated data are used to bootstrap the models creating 'prior models' that are further trained using much less real data than otherwise it would be required. The Bayesian nature of the approach let us do so. The predictive power of these models lets us categorize human actions very soon after the beginning of the action. Because of the generic nature of the typical behaviors of each of the implemented systems there is a reason to believe that this approach to modeling human behavior would generalize to other dynamic human-machine systems. This would allow us to recognize automatically people's intended action, and thus build control systems that dynamically adapt to suit the human's purposes better.by Nuria M. Oliver.Ph.D

Robot environment learning with a mixed-linear probabilistic state-space model

This thesis proposes the use of a probabilistic state-space model with mixed-linear dynamics for learning to predict a robot's experiences. It is motivated by a desire to bridge the gap between traditional models with predefined objective semantics on the one hand, and the biologically-inspired "black box" behavioural paradigm on the other. A novel EM-type algorithm for the model is presented, which is less compuationally demanding than the Monte Carlo techniques developed for use in (for example) visual applications. The algorithm's E-step is slightly approximative, but an extension is described which would in principle make it asymptotically correct. Investigation using synthetically sampled data shows that the uncorrected E-step can any case make correct inferences about quite complicated systems. Results collected from two simulated mobile robot environments support the claim that mixed-linear models can capture both discontinuous and continuous structure in world in an intuitively natural manner; while they proved to perform only slightly better than simpler autoregressive hidden Markov models on these simple tasks, it is possible to claim tentatively that they might scale more effectively to environments in which trends over time played a larger role. Bayesian confidence regions—easily by mixed-linear model— proved be an effective guard for preventing it from making over-confident predictions outside its area of competence. A section on future extensions discusses how the model's easy invertibility could be harnessed to the ultimate aim of choosing actions, from a continuous space of possibilities, which maximise the robot's expected payoff over several steps into the futur