Let's face it, robots are cool. They're also going to run the world some day, and hopefully, at that time they will take pity on their poor soft fleshy creators (a.k.a. I'm joking of course, but only sort of. In my ambition to have some small influence over the matter, I took a course in autonomous robot control theory last year, which culminated in my building a Python-based robotic simulator that allowed me to practice control theory on a simple, mobile, programmable robot. In this article, I'm going to show how to use a Python robot framework to develop control software, describe the control scheme I developed for my simulated robot, illustrate how it interacts with its environment and achieves its goals, and discuss some of the fundamental challenges of robotics programming that I encountered along the way. The snippets of code shown here are just a part of the entire simulator, which relies on classes and interfaces, so in order to read the code directly, you may need some experience in Python and object oriented programming. Finally, optional topics that will help you to better follow this tutorial are knowing what a state machine is and how range sensors and encoders work. The fundamental challenge of all robotics is this: It is impossible to ever know the true state of the environment. Robot control software can only guess the state of the real world based on measurements returned by its sensors. It can only attempt to change the state of the real world through the generation of control signals.

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The California tradition of summer fun -- barbecues, garden parties, group excursions to beaches and mountains -- is colliding with the state's desperate efforts to prevent new surges of coronavirus cases as the economy opens up and people begin freeing themselves from months of stay-at-home rules. Confirmed coronavirus cases have continued to climb as California allowed many businesses to reopen. But on Monday, Gov. Gavin Newsom said COVID-19 hospitalizations are also beginning to rise again statewide, a troubling shift that raises new questions about whether the reopening might need to be slowed. "Those that suggest we're out of the woods, those that suggest this somehow is going to disappear, these numbers tell a very, very different and sobering story," Newsom said. The number of people hospitalized with confirmed coronavirus infections in California was up 16% over the last two weeks, rising to 3,702 as of Sunday.

Natural language object retrieval is a highly useful yet challenging task for robots in human-centric environments. Previous work has primarily focused on commands specifying the desired object's type such as "scissors" and/or visual attributes such as "red," thus limiting the robot to only known object classes. We develop a model to retrieve objects based on descriptions of their usage. The model takes in a language command containing a verb, for example "Hand me something to cut," and RGB images of candidate objects and selects the object that best satisfies the task specified by the verb. Our model directly predicts an object's appearance from the object's use specified by a verb phrase. We do not need to explicitly specify an object's class label. Our approach allows us to predict high level concepts like an object's utility based on the language query. Based on contextual information present in the language commands, our model can generalize to unseen object classes and unknown nouns in the commands. Our model correctly selects objects out of sets of five candidates to fulfill natural language commands, and achieves an average accuracy of 62.3% on a held-out test set of unseen ImageNet object classes and 53.0% on unseen object classes and unknown nouns. Our model also achieves an average accuracy of 54.7% on unseen YCB object classes, which have a different image distribution from ImageNet objects. We demonstrate our model on a KUKA LBR iiwa robot arm, enabling the robot to retrieve objects based on natural language descriptions of their usage. We also present a new dataset of 655 verb-object pairs denoting object usage over 50 verbs and 216 object classes.

Created in collaboration with Epic Games. This"critically-acclaimed" and "insanely successful" Unreal Engine coursewas created in collaboration with Epic Games. The first three remastered sections have been released! New content will be released over the coming weeks and months. Existing students get all the new material for free.

You may like to look at our GeomLab website which will introduce you to some of the most important ideas in computer programming in an interactive, visual way through a guided activity. The Turtle system is a graphics programming environment designed to provide an enjoyable introduction to programming in Java syntax, together with a practical insight into fundamental concepts of computer science such as compilation and machine code. The Alice system from Carnegie Mellon University provides a point-and-click environment for designing 3-D animations and is a useful introduction to object-oriented programming. Elizabeth is an automated conversation and natural language processing program that provides an enjoyable introduction to natural language processing, and that can give insights into some of the fundamental methods and issues of artificial intelligence within an entertaining context. CodeAcademy provides a fun introduction to programming.

Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden representations, such as their ability to predict salient attributes or to generalise to unseen situations. To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations, in particular concerning attribute grounding. To this end, we extend the original GuessWhat?! dataset by including a semantic layer on top of the perceptual one. Specifically, we enrich the VisualGenome scene graphs associated with the GuessWhat?! images with abstract and situated attributes. By using diagnostic classifiers, we show that current models learn representations that are not expressive enough to encode object attributes (average F1 of 44.27). In addition, they do not learn strategies nor representations that are robust enough to perform well when novel scenes or objects are involved in gameplay (zero-shot best accuracy 50.06%).

Alice is an innovative block-based programming environment that makes it easy to create animations, build interactive narratives, or program simple games in 3D. Alice is designed to teach logical and computational thinking skills, fundamental principles of programming and to be a first exposure to object-oriented programming. The Alice Project provides supplemental tools and materials for teaching using Alice across a spectrum of ages and subject matter with proven benefits in engaging and retaining diverse and underserved groups in computer science education.