While there has been a lot of research recently on robots in household environments, at the present time, most robots in existence can be found on shop floors, and most interactions between humans and robots happen there. ``Collaborative robots'' (cobots) designed to work alongside humans on assembly lines traditionally require expert programming, limiting ability to make changes, or manual guidance, limiting expressivity of the resulting programs. To address these limitations, we explore using Large Language Models (LLMs), and in particular, their abilities of doing in-context learning, for conversational code generation. As a first step, we define RATS, the ``Repetitive Assembly Task'', a 2D building task designed to lay the foundation for simulating industry assembly scenarios. In this task, a `programmer' instructs a cobot, using natural language, on how a certain assembly is to be built; that is, the programmer induces a program, through natural language. We create a dataset that pairs target structures with various example instructions (human-authored, template-based, and model-generated) and example code. With this, we systematically evaluate the capabilities of state-of-the-art LLMs for synthesising this kind of code, given in-context examples. Evaluating in a simulated environment, we find that LLMs are capable of generating accurate `first order code' (instruction sequences), but have problems producing `higher-order code' (abstractions such as functions, or use of loops).

The manufacturing industry (largely) welcomed artificial intelligence with open arms. Planning for mechanical assemblies still requires more than scratching out some sketches, of course -- it's a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies. Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential for error. In a quest to ease some of said burdens, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that's accurate, efficient, and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multipart assembly, and then searches for a physically realistic motion path for each step.

The manufacturing industry (largely) welcomed artificial intelligence with open arms. However, planning for mechanical assemblies still requires more than scratching out some sketches, of course--it's a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies. Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential to be riddled with errors. In a quest to ease some of said burdens, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that's accurate, efficient and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multi-part assembly, and then searches for a physically realistic motion path for each step.

A new database would help engineers and manufacturers to apply machine learning to mechanical parts. Computer vision researchers use machine learning to train computers in visually recognizing objects--but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers. A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3-D mechanical parts, designed to help researchers apply machine learning to those parts in actual machines. "We are in the deep learning era, using computers to search for things visually," said Karthik Ramani, Purdue's Donald W. Feddersen Distinguished Professor of Mechanical Engineering. "But no one is focusing on the parts that go into machines: pipes, bearings, motors, washers, nuts and bolts, etc. Those are the things that are important to us as engineers and manufacturers. We want to be able to point a camera at a real-world part, and have the computer tell us everything about that part or design."

Less cleaning power than we'd expect at this price You can get the machine in white or graphite steel, and it's much less boxy than the average top-loader with pole agitator, showcasing sleek curves and a touchpad alongside its cycle selection knob for customizing wash cycles. Like most modern top-loaders, the WT7305CV comes with a Deep Fill feature, which fills up the tub significantly more than it would otherwise. While this may be helpful in certain circumstances--for example, if your clothes are completely covered in mud--on the whole adding more water actually results in your clothes getting less clean. As such, it's a feature that should be used sparingly, not left enabled as a default. The LG WT7305CV has a large 4.8 cu.

Amid the COVID-19 crisis, the global market for Robotic Flexible Washer estimated at US$904. 1 Million in the year 2020, is projected to reach a revised size of US$1.New York, July 30, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com Standalone Washers, one of the segments analyzed in the report, is projected to record 4.2% CAGR and reach US$691.4 After an early analysis of the business implications of the pandemic and its induced economic crisis, growth in the Modular Washers segment is readjusted to a revised 3.4% CAGR for the next 7-year period. The U.S. Market is Estimated at $243.8 Million, While China is Forecast to Grow at 6.9% CAGR The Robotic Flexible Washer market in the U.S. is estimated at US$243.8 Million in the year 2020. China, the world s second largest economy, is forecast to reach a projected market size of US$250.8

Here is a list of white papers. Please let us know if there is a white paper you would like to see that's not on the list. Just send us an email containing details about the white paper including Name, Publication Date, Contact Telephone, Email and URL if available. This 5G Americas' white paper explores Edge Computing's role in the evolution of 5G architecture, the application of Cloud-native principles such as software defined networking (SDN) and network function virtualization (NFV), and identifies various methodologies currently being adopted for 5G applications. It covers detailed emerging use cases and outlines the stringent requirements needed to facilitate advanced mobility, compute, storage capabilities for emerging 5G wireless networks.

Shop and save during The Home Depot's Memorial Day sale happening now. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Cookouts, backyard bashes, and getting to relish a hard-earned long weekend--these are just a few of the things that might leap to mind when you think about Memorial Day. But if you're someone who absolutely loves DIY projects and just generally doing stuff around the house, chances are you also think of Home Depot, since their annual Memorial Day sale is one of the best events they run all year.

As far as Google is concerned, there's no place like home. Ahead of its I/O Developer conference next week, Google announced that its artificial-intelligence-fused Google Assistant is now connected to more than 5,000 devices in the home, up from 1,500 in January. The idea is that you'll be able to summon the Google Assistant and issue voice commands, to preheat the oven, see who is at your front door, or dim the lights. The list of cloud-connected smart home products where the Google Assistant has an increased presence includes cameras, thermostats, security systems, vacuums, washers, and air conditioners. Of course, with the smart home rapidly emerging as one of the next key battlegrounds for tech, this very same vision is being laid out by Amazon with Alexa, Apple through HomeKit and Siri, Microsoft with Cortana, and Samsung via SmartThings and Bixby.

We're still diving deep into Europe's biggest tech show: the weekend included pianos that can talk with Alexa, and testing out both LG and Nokia's newest phones' photography -- including selfies. LG's new V30, unveiled at IFA 2017, is the first smartphone to offer a glass lens with a f/1.6 aperture, and has some rather cool video-recording tools that should excite aspiring Spielbergs. To see if it really lives up to the sales promises, Reviews Editor Cherlynn Low took the V30 on a trigger-happy tour of Berlin's Tier Garden, and found it a versatile, powerful camera. In fact, its filmmaking features are truly standout. It doesn't do justice to the label's imaging heritage.