Domain Randomization and Generative Models for Robotic Grasping

Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution over possible grasps. This model allows us to sample grasps efficiently at test time (or avoid sampling entirely). We evaluate our model architecture and data generation pipeline in simulation and the real world. We find we can achieve a $>$90% success rate on previously unseen realistic objects at test time in simulation despite having only been trained on random objects. We also demonstrate an 80% success rate on real-world grasp attempts despite having only been trained on random simulated objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018

Development of Techniques to Perform Simulation-Adaptation in a Simulation Training Environment Using Expert System Methods

The use of computers for instructional purposes is steadily increasing, along with an emphasis on developing systems which create environments tailored to human beings. Artificial Intelligence techniques have been incorporated into these systems with an aim at developing better methods of modeling of simulating knowledge and intelligent behavior. One type of these systems, Intelligent Simulation Training Systems (ISTS), utilize a simulation in the training process. This is an ideal environment for the instruction of skills which focus on the ability to understand the time and space relationships of objects. An intelligent tutor module of an ISTS must configure scenarios for the simulation which meet the objectives of the student\u27s current lesson. This document describes research efforts aimed at designing and implementing methods in which a tutor module intelligently configures scenarios off-line and then dynamically adapts these scenarios on-line as required, within the simulation

Modeling and simulation with augmented reality

In applications such as airport operations, military simulations, and medical simulations, conducting simulations in accurate and realistic settings that are represented by real video imaging sequences becomes essential. This paper surveys recent work that enables visually realistic model constructions and the simulation of synthetic objects which are inserted in video sequences, and illustrates how synthetic objects can conduct intelligent behavior within a visual augmented reality

Object oriented studies into artificial space debris

A prototype simulation is being developed under contract to the Royal Aerospace Establishment (RAE), Farnborough, England, to assist in the discrimination of artificial space objects/debris. The methodology undertaken has been to link Object Oriented programming, intelligent knowledge based system (IKBS) techniques and advanced computer technology with numeric analysis to provide a graphical, symbolic simulation. The objective is to provide an additional layer of understanding on top of conventional classification methods. Use is being made of object and rule based knowledge representation, multiple reasoning, truth maintenance and uncertainty. Software tools being used include Knowledge Engineering Environment (KEE) and SymTactics for knowledge representation. Hooks are being developed within the SymTactics framework to incorporate mathematical models describing orbital motion and fragmentation. Penetration and structural analysis can also be incorporated. SymTactics is an Object Oriented discrete event simulation tool built as a domain specific extension to the KEE environment. The tool provides facilities for building, debugging and monitoring dynamic (military) simulations

Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot

A methodology for simulation production systems considering the degree of autonomy

The increasing number of product varieties and declining product life cycles combined with individualised customer behaviour demand flexible and efficient production systems. A proper solution approach can be the use of intelligent technologies, capable of autonomous processing in order to react rapidly to changing requirements. However, production planners need a profound planning approach for the implementation of such technologies in production systems due to their cost intense investments. Therefore, simulation studies are suitable means for the analysis of a proper degree of autonomy in production systems. An appropriate methodology for the simulation of such systems is presented in this paper. The methodology is aligned with common guidelines on simulation studies and focuses on system analysis, formalisation and simulation. It is based on consistent methods – fact sheets and Value Stream Design for system analysis, Unified Modelling Language (UML) diagrams for formalisation and agent-based simulation. A central contribution to current research is the modular modelling of intelligence skills in production resources and parts in a simulation environment. Consequently, the developed methodology provides a basis for the implementation of simulation experiments in order to facilitate the evaluation of the economically efficient use of intelligent objects in production systems

Energy consumption management in Smart Homes: An M-Bus communication system

Energy consumption management in Smart Home environments relies on the implementation of systems of cooperative intelligent objects named Smart Meters. In order for devices to cooperate to smart metering applications' execution, they need to make their information available. In this paper we propose a framework that aims at managing energy consumption of controllable appliances in groups of Smart Homes belonging to the same neighbourhood or condominium. We consider not only electric power distribution, but also alternative energy sources such as solar panels. We define a communication paradigm based on M-Bus for the acquisition of relevant data by managing nodes. We also provide a lightweight algorithm for the distribution of the available alternative power among houses. Performance evaluation of experiments in simulation mode prove that the proposed framework does not jeopardise the lifetime of Smart Meters, particularly in typical situations where managed devices do not continuously turn on and off