Go Robot, Go: AI Team at MiR Helps Factory Robots Find Their Way

Like so many software developers, Elias Sorensen has been studying AI. Now he and his 10-member team are teaching it to robots.

When the AI specialists at Mobile Industrial Robots, based in Odense, Denmark, are done, the first graduating class of autonomous machines will be on their way to factories and warehouses, powered by NVIDIA Jetson Xavier NX GPUs.

“The ultimate goal is to make the robots behave in ways humans understand, so it’s easier for humans to work alongside them. And Xavier NX is at the bleeding edge of what we are doing,” said Sorensen, who will provide an online talk about MiR’s work at GTC Digital.

MiR’s low-slung robots carry pallets weighing as much as 2,200 pounds. They sport lidar and proximity sensors, as well as multiple cameras the team is now linking to Jetson Xavier GPUs.

Inferencing Their Way Forward

The new digital brains will act as pilots. They’ll fuse sensor data to let the bots navigate around people, forklifts and other objects, dynamically re-mapping safety zones and changing speeds as needed.

The smart bots use NVIDIA’s DeepStream and TensorRT software to run AI inference jobs on Xavier NX, based on models trained on NVIDIA GPUs in the AWS cloud.

MiR chose Xavier for its combination of high performance at low power and price, as well as its wealth of software.

“Lowering the cost and power consumption for AI processing was really important for us,” said Sorensen. “We make small, battery-powered robots and our price is a major selling point for us.” He noted that MiR has deployed more than 3,000 robots to date to users such as Ford, Honeywell and Toyota.

The new autonomous models are working prototypes. The team is training their object-detection models in preparation for first pilot tests.

Jetson Nano Powers Remote Vision

It’s MiR’s first big AI product, but not its first ever. Since November, the company has shipped smart, standalone cameras powered by Jetson Nano GPUs.

The Nano-based cameras process video at 15 frames per second to detect objects. They’re networked with each other and other robots to enhance the robots’ vision and help them navigate.

Both the Nano cameras and Xavier-powered robots process all camera data locally, only sending navigation decisions over the network. “That’s a major benefit for such a small, but powerful module because many of our customers are very privacy minded,” Sorensen said.

MiR developed a tool its customers use to train the camera by simply showing it pictures of objects such as robots and forklifts. The ease of customizing the cameras is a big measure of the product’s success, he added.

AI Training with Simulations

The company hopes its smart robots will be equally easy to train for non-technical staff at customer sites.

But here the challenge is greater. Public roads have standard traffic signs, but every factory and warehouse is unique with different floor layouts, signs and types of pallets.

MiR’s AI team aims to create a simulation tool that places robots in a virtual work area that users can customize. Such a simulation could let users who are not AI specialists train their smart robots like they train their smart cameras today.

The company is currently investigating NVIDIA’s Isaac platform, which supports training through simulations.

MiR is outfitting its family of industrial robots for AI.

The journey into the era of autonomous machines is just starting for MiR. It’s parent company, Teradyne, announced in February it is investing $36 million to create a hub for developing collaborative robots, aka co-bots, in Odense as part of a partnership with MiR’s sister company, Universal Robotics.

Market watchers at ABI Research predict the co-bot market could expand to $12 billion by 2030. In 2018, Danish companies including MiR and Universal captured $995 million of that emerging market, according to Damvad, a Danish analyst firm.

With such potential and strong ingredients from companies like NVIDIA, “it’s a great time in the robotics industry,” Sorensen said.

The post Go Robot, Go: AI Team at MiR Helps Factory Robots Find Their Way appeared first on The Official NVIDIA Blog.

Go Robot, Go: AI Team at MiR Helps Factory Robots Find Their Way

Like so many software developers, Elias Sorensen has been studying AI. Now he and his 10-member team are teaching it to robots. When the AI specialists at Mobile Industrial Robots, based in Odense, Denmark, are done, the first graduating class of autonomous machines will be on their way to factories and warehouses, powered by NVIDIA Read article >

The post Go Robot, Go: AI Team at MiR Helps Factory Robots Find Their Way appeared first on The Official NVIDIA Blog.

Researchers Make Movies of the Brain with CUDA

When colleagues told Sally Epstein they sped up image processing by three orders of magnitude for a client’s brain-on-a-chip technology, she responded like any trained scientist. Go back and check your work, the biomedical engineering Ph.D. told them.

Yet it was true. The handful of researchers at Cambridge Consultants had devised a basket of techniques to process an image on GPUs in an NVIDIA DGX-1 system in 300 milliseconds, a 3,000x boost over the 18 minutes the task took on an Intel Core i9 CPU.

The achievement makes it possible for researchers to essentially watch movies of neurons firing in real time using the brain-on-a-chip technology from NETRI, a French startup.

“Animal studies revolutionized medicine. This is the next step in testing for areas like discovering new drugs,” said Epstein, head of Strategic Technology at Cambridge Consultants, which develops products and technologies for a wide variety of established companies and startups such as NETRI.

The startup designs chips that sport 3D microfluidic channels to host neural tissues and a CMOS camera sensor with polarizing filters to detect individual neurons firing. It hopes its precision imaging can speed the development of novel treatments for neurological disorders such as Alzheimer’s disease.

Facing a Computational Bottleneck

NETRI’s chips generate 100-megapixel images at up to 1,000 frames per second — the equivalent of a hundred 4K gaming systems running at 120fps. Besides spawning tons of data, they use highly complex math.

As a result, processing a single second of a recording took NETRI 12 days, an unacceptable delay. So, the startup turned to Cambridge Consultants to bust through the bottleneck.

“Our track record in scientific and biological imaging turned out to be very relevant,” said Monty Barlow, Director of Strategic Technology at Cambridge Consultants. And when NETRI heard about the 3,000x boost, “they trusted us even though we didn’t trust ourselves at first,” he quipped.

Leveraging Math, Algorithms and GPUs

A handful of specialists at Cambridge Consultants delivered the 3,000x speedup using multiple techniques. For example, math and algorithm experts employed a mix of Gaussian filters, multivariate calculus and other tools to eliminate redundant tasks and reduce peak RAM requirements.

Software developers migrated NETRI’s Python code to CuPy to take advantage of the massive parallelism of NVIDIA’s CUDA software. And hardware specialists optimized the code to fit into GPU memory, eliminating unnecessary data transfers inside the DGX-1.

The CUDA profiler helped find bottlenecks in NETRI’s code and alternatives to resolve them. “NVIDIA gave us the tools to execute this work efficiently — it happened within a week with a core team of four researchers and a few specialists,” said Epstein.

Looking ahead, Cambridge Consultants expects to find further speedups for the code using the DGX-1 that could enable real-time manipulation of neurons using a laser. Researchers also aim to explore NVIDIA IndeX software to help visualize neural activity.

The work with NETRI is one of several DGX-1 applications at the company. It also hosts a Digital Greenhouse for AI research. Last year, it used the DGX-1 to create a low-cost but highly accurate tool for monitoring tuberculosis.

The post Researchers Make Movies of the Brain with CUDA appeared first on The Official NVIDIA Blog.

Researchers Make Movies of the Brain with CUDA

When colleagues told Sally Epstein they sped up image processing by three orders of magnitude for a client’s brain-on-a-chip technology, she responded like any trained scientist. Go back and check your work, the biomedical engineering Ph.D. told them. Yet it was true. The handful of researchers at Cambridge Consultants had devised a basket of techniques Read article >

The post Researchers Make Movies of the Brain with CUDA appeared first on The Official NVIDIA Blog.

A Taste for Acceleration: DoorDash Revs Up AI with GPUs

When it comes to bringing home the bacon — or sushi or quesadillas — DoorDash is shifting into high gear, thanks in part to AI.

The company got its start in 2013, offering deals such as delivering pad thai to Stanford University dorm rooms. Today with a phone tap, customers can order a meal from more than 310,000 vendors — including Chipotle, Walmart and Wingstop — across 4,000 cities in the U.S., Canada and Australia.

Part of its secret sauce is a digital logistics engine that connects its three-sided marketplace of merchants, customers and independent contractors the company calls Dashers. Each community taps into the platform for different reasons.

Using a mix of machine-learning models, the logistics engine serves personalized restaurant recommendations and delivery-time predictions to customers who want on-demand access to their local businesses. Meanwhile, it assigns Dashers to orders and sorts through trillions of options to find their optimal routes while calculating delivery prices dynamically.

The work requires a complex set of related algorithms embedded in numerous machine-learning models, crunching ever-changing data flows. To accelerate the process, DoorDash has turned to NVIDIA GPUs in the cloud to train its AI models.

Training in One-Tenth the Time

Moving from CPUs to GPUs for AI training netted DoorDash a 10x speed-up. Migrating from single to multiple GPUs accelerated its work another 3x, said Gary Ren, a machine-learning engineer at DoorDash who will describe the company’s approach to AI in an online talk at GTC Digital.

“Faster training means we get to try more models and parameters, which is super critical for us — faster is always better for training speeds,” Ren said.

“A 10x training speed-up means we spin up cloud clusters for a tenth the time, so we get a 10x reduction in computing costs. The impacts of trying 10x more parameters or models is trickier to quantify, but it gives us some multiple of increased overall business performance,” he added.

Making Great Recommendations

So far, DoorDash has discussed one of its deep-learning applications — its recommendation engine that’s been in production about two years. Recommendations are definitely becoming more important as companies such as DoorDash realize consumers don’t always know what they’re looking for.

Potential customers may “hop on our app and explore their options so — given our huge number of merchants and consumers — recommending the right merchants can make a difference between getting an order or the customer going elsewhere,” he said.

Because its recommendation engine is so important, DoorDash continually fine tunes it. For example, in its engineering blogs, the company describes how it crafts embedded n-dimensional vectors for each merchant to find nuanced similarities among vendors.

It also adopts the so-called multi-level, multi-armed bandit algorithms that let AI models simultaneously exploit choices customers have liked in the past and explore new possibilities.

Speaking of New Use Cases

While it optimizes its recommendation engine, DoorDash is exploring new AI use cases, too.

“There are several areas where conversations happen between consumers and dashers or support agents. Making those conversations quick and seamless is critical, and with improvements in NLP (natural-language processing) there’s definitely potential to use AI here, so we’re exploring some solutions,” Ren said.

NLP is one of several use cases that will drive future performance needs.

“We deal with data from the real world and it’s always changing. Every city has unique traffic patterns, special events and weather conditions that add variance — this complexity makes it a challenge to deliver predictions with high accuracy,” he said.

Other challenges the company’s growing business presents are in making recommendations for first-time customers and planning routes in new cities it enters.

“As we scale, those boundaries get pushed — our inference speeds are good enough today, but we’ll need to plan for the future,” he added.

The post A Taste for Acceleration: DoorDash Revs Up AI with GPUs appeared first on The Official NVIDIA Blog.

A Taste for Acceleration: DoorDash Revs Up AI with GPUs

When it comes to bringing home the bacon — or sushi or quesadillas — DoorDash is shifting into high gear, thanks in part to AI. The company got its start in 2013, offering deals such as delivering pad thai to Stanford University dorm rooms. Today with a phone tap, customers can order a meal from Read article >

The post A Taste for Acceleration: DoorDash Revs Up AI with GPUs appeared first on The Official NVIDIA Blog.

Meet Six Smart Robots at GTC 2020

The GPU Technology Conference is like a new Star Wars movie. There are always cool new robots scurrying about.

This year’s event in San Jose, March 22-26, is no exception, with at least six autonomous machines expected on the show floor. Like C3PO and BB8, each one is different.

Among what you’ll see at GTC 2020:

  • a robotic dog that sniffs out trouble in complex environments such as construction sites
  • a personal porter that lugs your stuff while it follows your footsteps
  • a man-sized bot that takes inventory quickly and accurately
  • a short, squat bot that hauls as much as 2,200 pounds across a warehouse
  • a delivery robot that navigates sidewalks to bring you dinner

“What I find interesting this year is just how much intelligence is being incorporated into autonomous machines to quickly ingest and act on data while navigating around unstructured environments that sometimes are not safe for humans,” said Amit Goel, senior product manager for autonomous machines at NVIDIA and robot wrangler for GTC 2020.

The ANYmal C from ANYbotics AG (pictured above), based in Zurich, is among the svelte navigators, detecting obstacles and finding its own shortest path forward thanks to its Jetson AGX Xavier GPU. The four-legged bot can slip through passages just 23.6 inches wide and climb stairs as steep as 45 degrees on a factory floor to inspect industrial equipment with its depth, wide-angle and thermal cameras.

Gita robot
The Gita personal robot will demo hauling your stuff at GTC 2020.

The folks behind the Vespa scooter will show Gita, a personal robot that can carry up to 40 pounds of your gear for four hours on a charge. It runs computer vision algorithms on a Jetson TX2 GPU to identify and follow its owner’s legs on any hard surfaces.

Say cheese. Bossa Nova Robotics will show its retail robot that can scan a 40-foot supermarket aisle in 60 seconds, capturing 4,000 images that it turns into inventory reports with help from its NVIDIA Turing architecture RTX GPU. Walmart plans to use the bots in at least 650 of its stores.

Mobile Industrial Robots A/S, based in Odense, Denmark, will give a talk at GTC about how it’s adding AI with Jetson Xavier to its pallet-toting robots to expand their work repertoire. On the show floor, it will demonstrate one of the robots from its MiR family that can carry payloads up to 2,200 pounds while using two 3D cameras and other sensors to navigate safely around people and objects in a warehouse.

From the other side of the globe, ExaWizards Inc. (Tokyo) will show its multimodal AI technology running on robotic arms from Japan’s Denso Robotics. It combines multiple sensors to learn human behaviors and perform jobs such as weighing a set portion of water.

Boss Nova robot
Walmart will use the Bossa Nova robot to help automate inventory taking in at least 650 of its stories

Rounding out the cast, the Serve delivery robot from Postmates will make a return engagement at GTC. It can carry 50 pounds of goods for 30 miles, using a Jetson AGX Xavier and Ouster lidar to navigate sidewalks like a polite pedestrian. In a talk, a Postmates engineer will share lessons learned in its early deployments.

Many of the latest systems reflect the trend toward collaborative robotics that NVIDIA CEO Jensen Huang demonstrated in a keynote in December. He showed ways humans can work with and teach robots directly, thanks to an updated NVIDIA Isaac developers kit that also speeds development by using AI and simulations to train robots, now part of NVIDIA’s end-to-end offering in robotics.

Just for fun, GTC also will host races of AI-powered DIY robotic cars, zipping around a track on the show floor at speeds approaching 50 mph. You can sign up here if you want to bring your own Jetson-powered robocar to the event.

We’re saving at least one surprise in robotics for those who attend. To get in on the action, register here for GTC 2020.

The post Meet Six Smart Robots at GTC 2020 appeared first on The Official NVIDIA Blog.

Meet Six Smart Robots at GTC 2020

The GPU Technology Conference is like a new Star Wars movie. There are always cool new robots scurrying about. This year’s event in San Jose, March 22-26, is no exception, with at least six autonomous machines expected on the show floor. Like C3PO and BB8, each one is different. Among what you’ll see at GTC Read article >

The post Meet Six Smart Robots at GTC 2020 appeared first on The Official NVIDIA Blog.

Life of Pie: How AI Delivers at Domino’s

Some like their pies with extra cheese, extra sauce or double pepperoni. Zack Fragoso’s passion is for pizza with plenty of data.

Fragoso, a data science and AI manager at pizza giant Domino’s, got his Ph.D. in occupational psychology, a field that employs statistics to sort through the vagaries of human behavior.

“I realized I liked the quant part of it,” said Fragoso, whose nimbleness with numbers led to consulting jobs in analytics for the police department and symphony orchestra in his hometown of Detroit before landing a management job on Domino’s expanding AI team.

The pizza maker “has grown our data science team exponentially over the last few years, driven by the impact we’ve had on translating analytics insights into action items for the business team.”

Making quick decisions is important when you need to deliver more than 3 billion pizzas a year — fast. So, Domino’s is exploring the use of AI for a host of applications, including more accurately predicting when an order will be ready.

Points for Pie, launched at last year’s Super Bowl, has been Domino’s highest profile AI project to date. Snap a smartphone picture of whatever pizza you’re eating and the company gave the customer loyalty points toward a free pizza.

“There was a lot of excitement for it in the organization, but no one was sure how to recognize purchases and award points,” Fragoso recalled.

“The data science team said this is a great AI application, so we built a model that classified pizza images. The response was overwhelmingly positive. We got a lot of press and massive redemptions, so people were using it,” he added.

Domino’s trained its model on an NVIDIA DGX system equipped with eight V100 Tensor Core GPUs using more than 5,000 images, including pictures some customers sent in of plastic pizza dog toys. A survey sent in response to the pictures helped automate some of the job of labeling the unique dataset now considered a strategic corporate asset.

AI Knows When the Order Will Be Ready

More recently, Fragoso’s team hit another milestone, boosting accuracy from 75% to 95% for predictions of when an order will be ready. The so-called load-time model factors in variables such as how many managers and employees are working, the number and complexity of orders in the pipeline and current traffic conditions.

The improvement has been well received and could be the basis for future ways to advance operator efficiencies and customer experiences, thanks in part to NVIDIA GPUs.

“Domino’s does a very good job cataloging data in the stores, but until recently we lacked the hardware to build such a large model,” said Fragoso.

At first, it took three days to train the load-time model, too long to make its use practical.

“Once we had our DGX server, we could train an even more complicated model in less than an hour,” he said of the 72x speed-up. “That let us iterate very quickly, adding new data and improving the model, which is now in production in a version 3.0,” he added.

More AI in the Oven

The next big step for Fragoso’s team is tapping a bank of NVIDIA Turing T4 GPUs to accelerate AI inferencing for all Domino’s tasks that involve real-time predictions.

Some emerging use cases in the works are still considered secret ingredients at Domino’s. However, the data science team is exploring computer vision applications to make getting customers their pizza as quick and easy as possible.

“Model latency is extremely important, so we are building out an inference stack using T4s to host our AI models in production. We’ve already seen pretty extreme improvements with latency down from 50 milliseconds to sub-10ms,” he reported.

Separately, Domino’s recently tapped BlazingSQL, open-source software to run data-science queries on GPUs. NVIDIA RAPIDS software eased the transition, supporting the APIs from a prior CPU-based tool while delivering better performance.

It’s delivering an average 10x speed-up across all use cases in the part of the AI process that involves building datasets.

“In the past some of the data-cleaning and feature-engineering operations might have taken 24 hours, but now we do them in less than an hour,” he said.

Try Out AI at NRF 2020

Domino’s is one of many forward-thinking companies using GPUs to bring AI to retail.

NVIDIA GPUs helped power Alibaba to $38 billion in revenue on Singles Day, the world’s largest shopping event. And the world’s largest retailer, Walmart, talked about its use of GPUs and NVIDIA RAPIDS at an event earlier this year.

Separately, IKEA uses AI software from NVIDIA partner Winnow to reduce food waste in its cafeterias.

You can learn more about best practices of using AI in retail at this week’s NRF 2020, the National Retail Federation’s annual event. NVIDIA and some of its 100+ retail partners will be on hand demonstrating our EGX edge computing platform, which scales AI to local environments where data is gathered — store aisles, checkout counters and warehouses.

The EGX platform’s real-time edge compute abilities can notify store associates to intervene during shrinkage, open new checkout counters when lines are getting long and deliver the best customer shopping experiences.

Book a meeting with NVIDIA at NRF here.

The post Life of Pie: How AI Delivers at Domino’s appeared first on The Official NVIDIA Blog.

Life of Pie: How AI Delivers at Domino’s

Some like their pies with extra cheese, extra sauce or double pepperoni. Zack Fragoso’s passion is for pizza with plenty of data. Fragoso, a data science and AI manager at pizza giant Domino’s, got his Ph.D. in occupational psychology, a field that employs statistics to sort through the vagaries of human behavior. “I realized I Read article >

The post Life of Pie: How AI Delivers at Domino’s appeared first on The Official NVIDIA Blog.