PCLIPS

CLIPS is an expert system, created specifically to allow rapid implementation of an expert system. CLIPS is written in C, and thus needs a very small amount of memory to run. Parallel CLIPS (PCLIPS) is an extension to CLIPS which is intended to be used in situations where a group of expert systems are expected to run simultaneously and occasionally communicate with each other on an integrated network. PCLIPS is a coarse-grained data distribution system. Its main goal is to take information in one knowledge base and distribute it to other knowledge bases so that all the executing expert systems are able to use that knowledge to solve their disparate problems

On quantifying the value of simulation for training and evaluating robotic agents

Un problème récurrent dans le domaine de la robotique est la difficulté à reproduire les résultats et valider les affirmations faites par les scientifiques. Les expériences conduites en laboratoire donnent fréquemment des résultats propres à l'environnement dans lequel elles ont été effectuées, rendant la tâche de les reproduire et de les valider ardues et coûteuses. Pour cette raison, il est difficile de comparer la performance et la robustesse de différents contrôleurs robotiques. Les environnements substituts à faibles coûts sont populaires, mais introduisent une réduction de performance lorsque l'environnement cible est enfin utilisé. Ce mémoire présente nos travaux sur l'amélioration des références et de la comparaison d'algorithmes (``Benchmarking'') en robotique, notamment dans le domaine de la conduite autonome. Nous présentons une nouvelle platforme, les Autolabs Duckietown, qui permet aux chercheurs d'évaluer des algorithmes de conduite autonome sur des tâches, du matériel et un environnement standardisé à faible coût. La plateforme offre également un environnement virtuel afin d'avoir facilement accès à une quantité illimitée de données annotées. Nous utilisons la plateforme pour analyser les différences entre la simulation et la réalité en ce qui concerne la prédictivité de la simulation ainsi que la qualité des images générées. Nous fournissons deux métriques pour quantifier l'utilité d'une simulation et nous démontrons de quelles façons elles peuvent être utilisées afin d'optimiser un environnement proxy.A common problem in robotics is reproducing results and claims made by researchers. The experiments done in robotics laboratories typically yield results that are specific to a complex setup and difficult or costly to reproduce and validate in other contexts. For this reason, it is arduous to compare the performance and robustness of various robotic controllers. Low-cost reproductions of physical environments are popular but induce a performance reduction when transferred to the target domain. This thesis present the results of our work toward improving benchmarking in robotics, specifically for autonomous driving. We build a new platform, the Duckietown Autolabs, which allow researchers to evaluate autonomous driving algorithms in a standardized framework on low-cost hardware. The platform offers a simulated environment for easy access to annotated data and parallel evaluation of driving solutions in customizable environments. We use the platform to analyze the discrepancy between simulation and reality in the case of predictivity and quality of data generated. We supply two metrics to quantify the usefulness of a simulation and demonstrate how they can be used to optimize the value of a proxy environment

Machine Learning in Robotic Navigation:Deep Visual Localization and Adaptive Control

The work conducted in this thesis contributes to the robotic navigation field by focusing on different machine learning solutions: supervised learning with (deep) neural networks, unsupervised learning, and reinforcement learning.First, we propose a semi-supervised machine learning approach that can dynamically update the robot controller's parameters using situational analysis through feature extraction and unsupervised clustering. The results show that the robot can adapt to the changes in its surroundings, resulting in a thirty percent improvement in navigation speed and stability.Then, we train multiple deep neural networks for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. We prepare two image-based localization datasets in 3D simulation and compare the results of a traditional multilayer perceptron, a stacked denoising autoencoder, and a convolutional neural network (CNN). The experiment results show that our proposed inception based CNNs without pooling layers perform very well in all the environments. Finally, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. The multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep CNNs. The results show a significant improvement when multi-goal reinforcement learning is used

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Move2Hear : active audio-visual source separation

We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple audio sources simultaneously (e.g., a person speaking down the hall in a noisy household) and must use its eyes and ears to automatically separate out the sounds originating from the target object within a limited time budget. Towards this goal, we introduce a reinforcement learning approach that trains movement policies controlling the agent’s camera and microphone placement over time, guided by the improvement in predicted audio separation quality. We demonstrate our approach in scenarios motivated by both augmented reality (system is already co-located with the target object) and mobile robotics (agent begins arbitrarily far from the target object). Using state-of-the-art realistic audio-visual simulations in 3D environments, we demonstrate our model’s ability to find minimal movement sequences with maximal payoff for audio source separation.Computer Science

A proposal for a global task planning architecture using the RoboEarth cloud based framework

As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this paper conceptualizes a global planning architecture, based on the worldwide accessible RoboEarth cloud framework. This architecture allows environmental state inference and plan monitoring on a global level. To enable plan re-use for future requests, the RoboEarth action language has been adapted to allow semantic matching of robot capabilities with previously composed plans

Scalable Life-long Visual Place Recognition

Visual place recognition (VPR) is the task of using visual inputs to determine if mobile robots are visiting a previously observed place or exploring new regions. To perform convincingly, a practical VPR algorithm must be robust against appearance changes, due to not only short-term (e.g., weather, lighting) and long-term (e.g., seasons, vegetation growth, etc) environmental variations, but also "less cyclical" changes (construction and roadworks, updating of signage, facades and billboards, etc). Such appearance changes invariably occur in real life. It motivates our thesis to fill this research gap. To this end, we firstly investigate probabilistic frameworks to effectively exploit the temporal information from visual data which is in the form of videos. Inspired by Bayes Filter, we propose two VPR methods that respectively perform filtering on discrete and continuous domains, where the temporal information is efficiently used to improve VPR accuracy under appearance changes. Given the fact that the appearance of operational environments uninterruptedly and indefinitely changes, a promising solution for VPR to deal with appearance changes is to continuously accumulate images to incorporate new changes into the internal environmental representation. This demands a VPR technique that is scalable on an ever growing dataset. To this end, inspired by Hidden Markov Models (HMM), we develop novel VPR techniques, that can be efficiently updated and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from the linear growth in time and space complexity. Another approach to address the scalability issue in VPR is map summarization, which only keeps informative 3D points in a topometric map, according to predefined constraints. In this thesis, we define timestamp as another constraint. Accordingly, we formulate a repeatability predictor (RP) as a regressor, that predicts the repeatability of an interest point as a function of time. We show that the RP can be used to significantly alleviate the degeneration of VPR accuracy from map summarization. The contributions of this thesis not only fill the gap within current state of VPR research; but, more importantly, also enable a wide range of applications, such as, self-driving cars, autonomous robots, augmented reality, and so on.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202