Master functions and discover how to write functional programs in R. In this concise book, you'll make your functions pure by avoiding side-effects; you'll write functions that manipulate other functions, and you'll construct complex functions using simpler functions as building blocks. In Functional Programming in R, you'll see how we can replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data.

In this work, we explore human-robot collaborative planning using the \emph{functional object-oriented network} (FOON), a graphical knowledge representation for manipulations that can be performed by domestic robots. The knowledge retrieval procedure, used for acquiring the necessary steps (as a task tree) to solve a given problem, is modified to account for weights that reflect the difficulty of performing motions in a universal FOON. These weights are given as success rates, which describe the likelihood of a robot successfully completing the action(s) on its own. However, certain manipulations may be too difficult for it to perform on its own based on its own physical limitations. To make it easier for the robot, a human can assist to the minimal extent needed to perform the activity to completion by identifying those actions with low success rates for the human to do. From our experiments, it is shown that tasks can be executed successfully with the aid of the assistant. Our results show that the best task tree can be found with the adequate chance of success in completing three activities while minimizing the effort needed from the human assistant.

Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.

Author Kishori Sharan walks you through writing your first Java program step-by-step. Armed with that practical experience, you'll be ready to learn the core of the Java language. Beginning Java 9 Fundamentals provides over 90 diagrams and 240 complete programs to help you learn the topics faster. The book continues with a series of foundation topics, including using data types, working with operators, and writing statements in Java. These basics lead onto the heart of the Java language: object-oriented programming.

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.

Object detection models shipped with camera-equipped mobile devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical feature for a robust and personalized mobile object detection system that many applications would rely on. In this paper, we present an efficient yet practical system, IMOD, to incrementally train an existing object detection model such that it can detect new object classes without losing its capability to detect old classes. The key component of IMOD is a novel incremental learning algorithm that trains end-to-end for one-stage object detection deep models only using training data of new object classes. Specifically, to avoid catastrophic forgetting, the algorithm distills three types of knowledge from the old model to mimic the old model's behavior on object classification, bounding box regression and feature extraction. In addition, since the training data for the new classes may not be available, a real-time dataset construction pipeline is designed to collect training images on-the-fly and automatically label the images with both category and bounding box annotations. We have implemented IMOD under both mobile-cloud and mobile-only setups. Experiment results show that the proposed system can learn to detect a new object class in just a few minutes, including both dataset construction and model training. In comparison, traditional fine-tuning based method may take a few hours for training, and in most cases would also need a tedious and costly manual dataset labeling step.

First we start off by learning the basics of Python and installing the required tools to write Python code. In this section we cover almost all the Python concepts in an in depth manner, where I will explain each and every line of code. There are over 50 lectures covering almost all the Python concepts. This includes all the concepts such as data structures, object oriented programming, functional programming, control flow, etc.

PHP 5's object-oriented enhancements are among the most significant improvements in the 10 year history of the language. This book introduces you to those features and the many opportunities they provide, as well as a number of tools that will help you maximize development efforts. The book begins with a broad overview of PHP 5's object-oriented features, introducing key topics like a class declaration, object instantiation, inheritance, and method and property encapsulation. You'll also learn about advanced topics including static methods and properties, abstract classes, interfaces, exception handling, object cloning, and more. The next part of the book is devoted to a topic that is often a natural extension of any object-oriented introduction: design patterns.

Python is a popular high-level object-oriented programming language which is used widely by a huge number of software developers. Guido van Rossum designed this in 1991, and Python software foundation has further developed it. But the question is, with dozens of programming languages based on OOP concepts already available, why this new one? So, the main purpose to develop this language is to emphasize code readability and scientific and mathematical computing (e.g. Python's syntax is very clean and short in length.

Have you ever wanted to build a games with a graphical interface but didn't know how to? May be you even know how to create tools on a command line but have no idea how to convert it into a graphical interface that people can click on. In this course we will be learning Python GUI Programming Turtle other advanced python modules to build graphical user interfaces (GUI) and games from scratch. We will learn from basics of Python i.e. variables, slicing, string, some module, arithmetic and logical operations, looping, functions, object oriented programming. After that we will learn the basics stuff of Pygame and OpenGL and Blender basics stuff.