Covariant raised another $75 million in its Series C funding round. The company announced the first part of its series C round, totaling $80 million, in 2021. It has now raised $222 million since its founding.

Radical Ventures and Index Ventures, both returning investors in the company, co-led the round. The most recent round also included participation from other returning investors Canada Pension Plan Investment Board and Amplify partners, and new investors Gates Frontier Holdings, AIX Ventures and Northgate Capital.

Since its founding in 2017, Covariant has focused on developing the Covariant Brain, which the company called a Universal AI platform. Pre-trained on millions of picks from Covariant robots in warehouses around the world, the Covariant Brain enables robots to autonomously pick many SKUs. Covariant plans to use the funding to ensure its platform will allow retail and logistics providers to deploy robotic picking quickly.

“The leading companies have turned to AI Robotics to automate their most manual operations in order to decrease labor costs, increase throughput, and control profitability,” said Covariant CEO Peter Chen. “The past year for Covariant has been incredible with 6x growth in 2022 – and we are just getting started. This infusion of new capital allows us to scale even faster, ensuring more retailers can automate more parts of their fulfillment networks to remove manual bottlenecks, handle fluctuating demand, and better prepare for ever-changing business needs.”

Since its last funding announcement in 2021, the company has applied Covaraint Brian to a broad set of piece-picking and case-picking applications, including order sortation, item induction, good-to-person order picking, knitting and depalletization. With the platform, connected robots learn as a fleet, enabling operational improvements to automatically propagate across customers’ networks.

“Many companies are trying to break into the AI robotics space, but Covariant has been making significant progress for years now,” said Mike Volpi, partner at Index Ventures. “I’m confident that their team, which represents the best minds in AI, and their approach of deploying a unified AI platform are shaping the future of automation, and look forward to the additional progress they’ll make in the years ahead.”

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The MIT team tested its collision avoidance system in a flight environment with six drones and in simulation. | Source: MIT

A research team from MIT created a trajectory-planning system called Robust MADER that can allow drones working together in the same airspace to pick safe paths forward without crashing into each other. The algorithm is an updated version of MADER, a 2020 project that worked well in simulation but didn’t hold up in real-world testing. 

The original MADER system involved each agent broadcasting its trajectory so fellow drones know where it’s planning to go. In simulation, this worked without problems, with all drones considering each other’s trajectories when planning their own. When put to the test, the team found that it didn’t take into account delays in communication between drones, resulting in unexpected collisions. 

“MADER worked great in simulations, but it hadn’t been tested in hardware. So, we built a bunch of drones and started flying them. The drones need to talk to each other to share trajectories, but once you start flying, you realize pretty quickly that there are always communication delays that introduce some failures,” Kota Kondo, an aeronautics and astronautics graduate student, said.

Robust MADER is able to generate collision-free trajectories for drones even when there is a delay in communications between agents. The system is an asynchronous, decentralized, multiagent trajectory planner, meaning each drone formulates its own trajectory and then checks with drones nearby to ensure it won’t run into any of them. 

The drones optimize their new trajectories using an algorithm that incorporates the trajectories they received from nearby drones, and agents constantly optimize and broadcast new trajectories to avoid collisions. 

To get around any delays in sharing trajectories, every drone has a delay-check period, where it spends a fixed amount of time repeatedly checking for communications from other agents to see if its new trajectory is safe. If it finds a possible collision, it abandons the new trajectory and keeps going on its current one. The length of this delay-check period depends on the distance between agents and other environmental factors that could hamper communications. 

While the system does require all drones to agree on each new trajectory, they don’t all have to agree at the same time, making it a scalable system. It could be used in any situation where multiple drones are working together in the same airspace like spraying pesticides over crops. 

The MIT team ran hundreds of simulations in which they artificially introduced communication delays, and found that MADER was 100% successful at avoiding collisions. When tested with six drones and two aerial obstacles in a flight environment, Robust MADER was able to avoid all collisions, while the old algorithm would have caused seven collisions. 

Moving forward, the research team hopes to put Robust MADER to the test outdoors, where obstacles can affect communications. They also hope to outfit drones with visual sensors so they can detect other agents or obstacles, predict their movements and include that information in trajectory optimizations. 

Kota Konda wrote the paper with Jesus Tordesillas, a postdoc; Parker C. Lusk, a graduate student; Reinaldo Figueroa, Juan Rached, and Joseph Merkel, MIT undergraduates; and senior author Jonathan P. How, the Richard C. Maclaurin Professor of Aeronautics and Astronautics, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and a member of the MIT-IBM Watson AI Lab. This work was supported by Boeing Research and Technology.

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A screenshot of the Roboto platform.

Roboto AI, a company working to create tools to help robotics developers go from a prototype to a reliable production system, emerged from stealth with $4.8 million in Seed funding. Unusual Ventures led the round, which also included the Allen Institute for Artificial Intelligence (AI2) and FUSE Ventures. Roboto AI is also partnering with researchers and professors at the ETH Zürich AI Center. 

Robotics companies oftentimes have to develop all of their infrastructure, anything from custom hardware to data infrastructure in-house. This could cost startups crucial time and money that could be spent getting their systems into the world. Roboto AI hopes to make development easier by providing a platform that supports robotics data. 

The company’s founders, Benji Barash (CEO) and Yves Albers-Schoenberg (CTO) have experience building this kind of infrastructure from their time at Amazon Robotics. The two used to spend entire days writing scripts to filter and transform sensor data so they could debug system failures, evaluate performance and create new algorithms. 

Roboto AI is working to create a data platform that allows users to easily search across modalities using natural language. The company currently has a free demo available that uses data from nuScenes, a large-scale, autonomous driving dataset used by engineers and researchers, for those interested in testing an early version of Roboto. With the sandbox, users can not only use natural language to search for the data they need, but they can also perform searches on graphical time-series signals. 

Roboto AI was founded in 2022 when it spun out of the Allen Institute for Artificial Intelligence. It is based in Seattle, Washington.

“Robotics is hard, but it shouldn’t be this hard. We want to catalyze progress in the robotics industry and see robots get to production faster by getting AI to do the dirty work for us,” the founders wrote in a blog. “We’re building Roboto to be an out-of-the-box solution to the data challenges engineers face daily and we’re excited to have the support of an incredible group of advisors and investors to make it happen. We’re building new AI-powered tools for anyone working with sensor or log data; think copilot for robotics.”

Roboto AI CTO Yves Albers-Schoenberg (left) and CEO Benji Barash. | Source: Roboto AI

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RGo Robotics, a company developing artificial perception technology that enables mobile robots to understand complex surroundings and operate autonomously, announced significant strategic updates. The announcements include leadership appointments, new customers and an upcoming product release.

RGo develops AI-powered technology for autonomous mobile robots, allowing them to achieve 3D, human-level perception. Its Perception Engine gives mobile robots the ability to understand complex surroundings and operate autonomously. It integrates with mobile robots to deliver centimeter-scale position accuracy in any environment. In Q2 2023, RGo said it will release the next iteration of its software that will include:

• An indoor-outdoor mode: a breakthrough capability for mobile robot navigation allows them to operate in all environments – both indoors and outdoors.
• A high-precision mode that enables millimeter-scale precision for docking and similar use cases.
• Control Center 2.0: a redesigned configuration and admin interface. This new version supports global map alignment, advanced exploration capabilities and new map-sharing utilities.

RGo separately announced support for NVIDIA Jetson Orin System-on-Modules that enables visual perception for a variety of mobile robot applications.

RGo will exhibit its technology at LogiMAT 2023, Europe’s biggest annual intralogistics tradeshow, from April 25-27, in Stuttgart, Germany at Booth 6F59. The company will also sponsor and host a panel session “Unlocking New Applications for Mobile Robots” at the Robotics Summit and Expo in Boston from May 10-11.

Leadership announcements

RGO also announced four leadership appointments. This includes Yael Fainaro being named chief business officer and president; Mathieu Goy being named head of European sales; Yasuaki Mori being named executive consultant, APAC market development; and Amy Villeneuve as a member of the board of directors.

“It is exciting to have reached this important milestone. The new additions to our leadership team underpin our evolution from a technology innovator to a scaling commercial business model including new geographies,” said Amir Bousani, CEO and co-founder, RGo Robotics.

Goy, based in Paris, and Mori, based in Tokyo, join with extensive sales experience in the European and APAC markets. RGo is establishing an initial presence in Japan this year with growth in South Korea planned for late 2023.

“RGo has achieved impressive product maturity and growth since exiting stealth mode last year,” said Fainaro. “The company’s vision-based localization capabilities are industrial-grade, extremely precise and ready today for even the most challenging environments. This, together with higher levels of 3D perception, brings tremendous value to the rapidly growing mobile robotics market. I’m looking forward to working with Amir and the team to continue growing RGo in the year ahead.”

Villeneuve joins RGo’s board of directors with leadership experience in the robotics industry, including her time as the former COO and president of Amazon Robotics. “I am very excited to join the team,” said Villeneuve. “RGo’s technology creates disruptive change in the industry. It reduces cost and adds capabilities to mobile robots in logistics, and enables completely new applications in emerging markets including last-mile delivery and service robotics.”

Customer traction

After comprehensive field trials in challenging indoor and outdoor environments, RGo continued its commercial momentum with new customers. The design wins are with market-leading robot OEMs across multiple vertical markets ranging from logistics and industrial autonomous mobile robots, forklifts, outdoor machinery and service robots.

Capra Robotics, an award-winning mobile robot manufacturer based in Denmark, selected RGo’s Perception Engine for its new Hircus mobile robot platform.

“RGo continues to develop game-changing navigation technology,” said Niels Juls Jacobsen, CEO of Capra and founder of Mobile Industrial Robots. “Traditional localization sensors either work indoors or outdoors – but not both. Combining both capabilities into a low-cost, compact and robust system is a key aspect of our strategy to deliver mobile robotics solutions to the untapped ‘interlogistics’ market.”

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Yaskawa’s Pallet Builder extension (left) and a Yaskawa robot using the company’s new MotoPick 4 software (right). | Source: Yaskawa

Yaskawa released its MotoPick 4 software system that adds no-code capabilities for high-speed pick and place. The company is also expanding its Smart Pendant’s capability with its new Pallet Builder extension. 

With the building blocks to create systems for up to 16 robots and up to 6 conveyors or 16 tables per robot, the Yaskawa MotoPick 4 software can synchronize multiple robots equipped with vision to pick fast-moving products off a conveyor and place them on an outfeed device, tray or box in an organized arrangement.
 
MotoPick 4 provides precise control and coordination for conveyor speed, accommodating tracking speeds up to 1 M/sec. If the advancement of either the infeed or outfeed conveyor falls behind, the software can be configured with an area that slows or stops conveyors until the operation is equalized. Additionally, the software features a pause function that allows for the system to be paused and later resumed without losing track of item positions.
 
Dynamic load balancing for single or multiple robot systems is accommodated. The ability to asynchronously pick up to four parts with multiple placements per cycle is also offered, with up to 32 different product types to be easily picked and sorted. The gripper is allowed up to eight zones.
 
Supporting multiple encoders and cameras, a robot interface board reduces the integration wiring and panel real estate required for multiple robot cells. Cameras are supported based on conveyor width, workpiece size, target accuracy, production volume and conveyor speed, and they do not need to be aligned in a pre-defined way.
 
Multi-layer tray support allows multiple-layer pattern picking or placing with trays. MotoPick 4 is compatible with the FS100 and YRC1000micro controllers, as well as the MLX300 controller software solution.

Pallet Builder

Expanding the Smart Pendant capability, Pallet Builder enables the development and deployment of palletizing cells. Designed for single pick-and-place tasks, this user interface uses a robust database and guided prompts to facilitate no-code programming for work cell configuration and pallet pattern setup.
 
Designed for small- to medium-sized enterprises with high-mix, low-to-medium-volume production that may have insufficient access to experienced robot programmers, Pallet Builder provides general functions for cell definition, gripper definition, and single pick and place jobs. Multiple infeeds, build stations and dispenser stations (for slip sheet separators) support up to eight combined stations.
 
Job editing is available for specific system requirements. Pallet Builder can accommodate a variety of grippers and end-of-arm tooling and adjusts for product variation. Capable software includes multiple automatic pattern styles as well as full custom pattern support. Single drop positions with selectable label orientation are also available.
 
Compatible with the YRC1000 and YRC1000 micro controllers, Pattern Builder is available as a complimentary download for use with GP-series and HC-series robots. Smart Pendant V3.0 or newer is required.

Yaskawa Motoman launched its newest robot in the PL-series line, the PL800, in October 2022. This industrial robot features an 800 kg (2,140 lb) payload capacity and is designed for a variety of palletizing applications, layer picking, and other logistical tasks for end-of-line or distribution automation.

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What is the best architectural approach to use when developing medical robots? Is it ROS, ROS 2 or other open-source or commercial frameworks? The upcoming Robotics Summit & Expo (May 10-11 in Boston) will explore engineering questions concerning the level of concern, risk, design controls, and evidence on a couple of different applications of these frameworks.

In a session on May 10 from 2-2:45 PM, Tom Amlicke, Software Systems Engineer, MedAcuity will discuss the “Keys to Using ROS 2 and Other Frameworks for Medical Robots.” Amlicke will look at three hypothetical robotic systems and explore these approaches:

• 1. An application based on the da Vinci Research Kit through regulatory clearance
• 2. ROS as test tools to verify the software requirements for a visual guidance system
• 3. Commercial off-the-shelve robot arm used for a medical application

If you attend his session, you will also learn how to create trade-offs with these different architectural approaches and how to validate the intended uses of these architectural approaches to ensure a successful submission package for your FDA, EMA, or other regulatory approval.

Amlicke has 20-plus years of embedded and application-level development experience. He designs and deploys enterprise, embedded, and mobile solutions on Windows, Mac, iOS, and Linux/UNIX platforms using a variety of languages including C++. Amlicke takes a lead role on complex robotics projects, overseeing end-to-end development of ROS-based mobile robots and surgical robots.

You can find the full agenda for the Robotics Summit here. The Robotics Summit & Expo is the premier event for commercial robotics developers. There will be nearly 70 industry-leading speakers sharing their development expertise on stage during the conference, with 150-plus exhibitors on the showfloor showcasing their latest enabling technologies, products and services that help develop commercial robots. There also will be a career fair, networking opportunities and more. 

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Ambi Robotics warehouse automation solution automates item picking operations. | Credit: Ambi Robotics

AmbiAccess, a cloud-based business intelligence tool from Ambi Robotics, helps customers maximize their Robots-as-a-Service (RaaS) investment. AmbiAccess optimizes operations and maximizes efficiency with superior fleet management and real-time analytics for global shippers and logistics organizations.

“We built the AmbiAccess platform with a suite of operation-enhancing applications to make our customers’ lives easier,” explains Ambi Robotics co-founder and CTO Jeff Mahler. “This new customer portal goes beyond data visibility, with comprehensive insights and advanced analytics that leverage historical data and trends. Now our customers can better understand the performance of their entire fleet, compare KPIs between facilities and regions, and access real-time analytics from any device.”

As companies deploy AI-powered robotic sorting systems across their networks, the AmbiAccess platform simplifies fleet management. On-demand access to data-driven business intelligence helps customers understand their operations. Customers may view real-time robot fleet performance, advanced analytics reports, actionable insights, the system’s knowledge library, and 24/7 customer assistance in one spot. Real-time data records productivity, system utilization, item analysis, and proactive insights to identify potential issues.

“AmbiAccess opens the doors to a more efficient and sustainable supply chain,” says Ambi Robotics VP of Operations Stephen McKinley. “This new platform demonstrates the impact of our AI-powered robotic sorting systems and allows our customers to gain an even greater competitive edge in the market.”

AmbiAccess automates throughput, sort plans, exception items, and operational effectiveness. Customers can compare system, facility, and regional trends and metrics. The cloud-based platform lets warehouse managers and operations executives view metrics from anywhere.

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NVIDIA Omniverse enables Amazon Robotics engineers to quickly simulate warehouse environments and train sensor data. | Credit: NVIDIA

NVIDIA’s CEO Jensen Huang presented the latest product announcements during his keynote at the 2023 GTC event this morning.

One of the first significant announcements is that NVIDIA accelerated computing together with cuOpt has solved Lee and Lim’s traveling salesman problem faster than any other solution to date. This milestone opens up a new world of capability for roboticists to create real-time solutions to AMR path planning problems.

Isaac Sim on Omniverse

Omniverse Cloud for enterprises is now available as a platform as a service (PaaS) for compute-intensive workloads like synthetic data generation and CI/CD. This PaaS provides access to top-of-the-line hardware when you need it for processing-intensive workloads. The service is rolling out with MS Azure.

Amazon Robotics is an early customer of Isaac Sim on Omniverse. The Proteus warehouse robot development team created a complete digital twin of the Proteus AMR and deployed it into Isaac SIM to help with the development and programming of the AMR. 

The team generated hundreds of photo-realistic environments to train and test sensor processing algorithms, and AMR behavior. This enabled the development team to accelerate the project without the need to build and test expensive prototypes in the real world.

Isaac Sim enables sensor simulation and interactive design and can run on AWS RoboMaker, to help with world generation. It is deployable on your cloud service provider.

BMW is also using NVIDIA Omniverse to accelerate the planning and design of new automobile assembly factories. BMW moved the virtual factory development to Omniverse. There, manufacturing engineers are able to layout robotics assembly workcells and virtually modify the robotics, tools and programming to optimize the workflow. Mercedes is claiming that this digital twin development process is shaving two years off the planning cycle for a new factory.

BMW is an early user of NVIDIA Omniverse for the development and programming of future automotive assembly lines and factories. | Credit: NVIDIA

Isaac ROS DP3 release adds new perception capabilities and open-source modules

There is a new ROS DP3 release for Issac that includes a number of new features:

• New LIDAR-based grid localizer package
• New people detection support in the NVBLOX package
  • GPU-accelerated 3D reconstruction for collision avoidance
• Updated VSLAM and depth perception GEM
• Source release of NITROS, NVIDIA’s ROS 2 hardware acceleration implementation
• New Isaac ROS benchmark suite built

NVIDIA Omniverse and Isaac Sim enable roboticists to view the world as the sensors see it. | Credit: NVIDIA

Updates to NVIDIA Jetson Orin Family

The Jetson Orin product line gets an update including a new Orin Nano unit that is available in a complete range of system-on-module from hobbyists to commercial platforms:

• Jetson Orin Nano 8GB/4GB
• Orin NX 16GB/8GB
• AGX Orin 64GB/32GB

New entry-level Jetson developer kit for Robotics / Edge AI

NVIDIA is introducing the NVIDIA Jetson Orin Nano developer kit that delivers 80x the performance when compared with the previous-generation Jetson Nano, enabling developers to run advanced transformer and robotics models. It also improves power efficiency by 50x the performance per watt, so developers getting started with the Jetson Orin Nano modules can build and deploy power-efficient, entry-level AI-powered robots, smart drones, and intelligent vision systems.

• Available now for preorder for $499

NVIDIA Metropolis

In a future-looking statement, NVIDIA believes that building infrastructure will evolve such that every building will be considered to be a “robot.” Practically, this implies that buildings and other infrastructure elements will be imbued with the ability to sense, think and act.

It starts with the idea to automate infrastructure with vision-based AI as a platform for things that watch other things that move. This is a vision that the company calls “NVIDIA Metropolis”

The company is announced the latest generation TAO, TAO 5.0 and the next version of DeepStream which puts more sensors to work to help automate machinery and solve computer vision grand challenges with APIs.

Additional features of TAIO 5.0 include:

• New transformer-based pre-trained models
• Deploy on any device – GPUs, CPUs, MCUs
• TAO is now source open
• AI-assisted annotation
• REST APIs
• Integration with any cloud — Google Vertex AI, AzureAI, AWS, etc.

NVIDIA is also announcing Metropolis Microservices to solve hard problems like multi-camera tracking and human and machine interactions.

DeepStream is putting AI to work in low-code interfaces for graph composing as an expansion of existing AI services. This includes multi-sensor sensor fusion and deterministic scheduling for things like PLC controllers.

New features of DeepStream SDK include a new graph execution runtime (GXF) that allows developers to expand beyond the open-source GStreamer multimedia framework. This update unlocks a host of new applications, including those in industrial quality control, robotics and autonomous machines.

Editors note: An earlier version of this article mistakenly referenced Mercedes instead of BMW. It has been updated to accurately document Jensen’s keynote references.

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Data science-driven platform seamlessly orchestrates large fleets of multiple robot form factors in very large warehouses to deliver predictable, efficient, and scalable productivity results. | Credit: Locus Robotics

Locus Robotics announces LocusONE, a seamless warehouse management solution that enables the operation and management of large quantities of multiple AMR form factors as a single, coordinated fleet in all sizes of warehouses. LocusONE uses proprietary data science to support the full breadth of material movement needs in today’s fulfillment and distribution warehouses.

“LocusONE makes it easy to deploy and manage large numbers of AMRs — and multiple form factors — within very large warehouses to work together as a single, orchestrated fleet,” said Rick Faulk, CEO of Locus Robotics. “Based on Locus’s proprietary data science engine, LocusONE enables operators to gain the flexibility and critical business intelligence needed to efficiently handle material payloads from 3 ounces to 3,000 pounds.”

The LocusONE platform supports a thousand or more robots, operating in sites as large as one million square feet or more, executing multiple use cases simultaneously in a single, intelligent, and orchestrated solution. With LocusONE, LocusBots engage in various tasks — including each picking and putaway, case picking and putaway, replenishment, pallet building, routine routes, point-to-point transport, counting, and more — within a single warehouse. The data science behind the scenes optimizes the mix of tasks throughout the day to achieve optimal warehouse productivity.

LocusONE integrates with any WMS system to provide flexible and dynamic fleet management and enables easy deployment of a mix of Locus Origin, Vector, and Max AMRs tailored to meet each warehouse’s specific needs. Regular enhancements are released as over-the-air updates to Locus’s installed base of more than 250 customer sites.

“Locus lets DHL Supply Chain deploy the right bot for our customers’ varied needs, all centrally coordinated and managed on one platform. With Locus’s powerful data science strategy, delivered through the LocusView dashboards, LocusONE plays an important role in providing the key business intelligence insight we need to optimize operational efficiency and improve service quality for our customers,” said Sally Miller, CIO North America & DSC Digital Transformation Officer of DHL Supply Chain North America. “Partnering with Locus has helped us deliver on our commitment to continuous innovation and digital transformation across the entire supply chain”.

Locus’s powerful data science foundation makes it possible to smoothly navigate hundreds – or even a thousand or more – bots in a single footprint as well as deliver predictive and actionable management guidance – in real time – to dramatically improve throughput across a multitude of use cases. LocusONE includes Locus’s award-winning LocusView package, which delivers data-driven, actionable insights across more than two dozen insightful reports and real-time dashboards, including labor guidance, predictive insights for work completion, operational comparisons against targets or time periods, order pool tracking and guidance, mission analysis and optimization, key performance visualization, and more.

LocusONE further extends Locus’s position as the industry’s AMR leader for automation and digitalization of warehouses, distribution, and fulfillment centers to efficiently meet increasing order volumes, labor shortages, and rising consumer expectations.

“As warehouses become increasingly complex, the ability to rely on a dependable data analytics strategy is essential to their smooth operation. Having the ability to deploy a flexible, seamless and proven AMR management platform to meet a broad range of use cases has become a must-have in the warehouse fulfillment industry,” said Ash Sharma, Senior Research Director at Interact Analysis. “This innovative platform demonstrates how critical multi-form factor interoperability is in today’s fulfillment warehouse and reflects Locus Robotics’ commitment to innovation, ease of use, and broad industry vision over the past few years.”

“LocusONE’s ability to integrate rapidly and efficiently with other automation technology – such as sortation or packaging systems – ensures that a nimble, scalable robotics solution can be easily deployed into both brownfield and greenfield environments,” noted Faulk.

“The LocusONE platform enables Kenco to create a seamless fulfillment experience that delivers enhanced productivity while improving employee morale and visibility. We rely on the LocusView dashboards to monitor progress and inform our labor management throughout the day,” said Kristi Montgomery, Vice President, Innovation, Research & Development Kenco Logistics. “LocusONE’s real-time insights allow us to delight our customers, and that’s what matters most to Kenco.”

LocusONE is available through the company’s all-inclusive, Robots-as-a-Service (RaaS) business model. Locus Origin, Vector, and Max can be easily added to existing and new workflows, enabling operations to dynamically scale and adapt to changing market demands. In a study done by Peerless Research Group, nearly half of the respondents said they would prefer to buy their robotics solution as an entire integrated system that includes hardware, software, support, and maintenance.

Locus’s Recycle, Refurbish, and Repurpose initiative is designed to drive sustainability actively across all aspects of the business — from manufacturing and deployment to support and maintenance — at sites around the world. Locus is continually working to identify and implement best-practice strategies and tactics designed to reduce overall waste across all areas of our organization.

Locus will be showcasing LocusONE at Promat, Booth #S2303, the material handling industry’s premier event, running March 20-23 in Chicago, IL, where the Locus theme will be Vision, Intelligence, Results – emphasizing Locus’s commitment to delivering actionable intelligence in an innovative solution to drive results for customers. Locus will also host guests at the Locus Theater, featuring a lineup of informative industry speakers and presentations, discussions with Locus’s customers and partners, as well as showcasing live picking and putaway demos.

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Clearpath Robotics, a manufacturer of mobile robotics platforms for research and development, announced Husky Observer, a fully integrated system designed to accelerate inspection solutions. This new configuration of the Husky aims to enable robotics developers and technology groups to build their inspection solutions and fast-track their system development.

Built on Top of the Versatile Husky Base Platform

Husky UGV is a tried and tested base platform that is trusted by hundreds of robotics researchers and engineers worldwide. With its simple and rugged design, Husky UGV is built to endure all real-world terrain making it ideal for observation missions. Fully supported in ROS, the robot can program complex autonomous systems.

As a result of past successful integration projects, the Husky Observer has been designed to showcase the flexibility of the Husky UGV. Pre-installed with Clearpath’s OutdoorNav Autonomy Software, the Husky Observer is built for all types of outdoor autonomous missions including but not limited to equipment monitoring, remote virtual inspection and detailed unmanned data collection.

“For more than a decade we have helped our customers succeed in their robotics projects. We’re excited to showcase the Husky’s versatility as well as bring our experience and capabilities to remote inspection where our products can accelerate the industry,” Bryan Webb, President of Clearpath Robotics, said.

Husky Observer: Inspection Ready

The Husky Observer comes with a variety of sensors, allowing users to program points of interest and capture data for offline review. Below are data capture stills from our Robotic Observation Systems:

Outdoor Thermal Sensor Data

Indoor LiDAR Data on RViz

Layered Thermal Sensor Data for Leak Detection

The Husky Observer is pre-installed with Clearpath’s OutdoorNav Autonomy Software enabling developers to plan and execute missions with an easy-to-use web interface for autonomous navigation in outdoor environments. The web-based user interface also supports teleoperation mode, allowing users to control their vehicle remotely. OutdoorNav supports several inspection actions out of the box, such as audio, image and video capture, and optional thermal capture. It provides an interface for users to integrate their own custom actions. The integrated system comes with a full-featured ROS Application Programming Interface (API) allowing advanced users to extend the capabilities of the system with their own add-ons. 

The Husky Observer is best deployed by tech-savvy personnel for:

• Remote virtual inspection, data sampling and image capture of infrastructure, high-value assets and safety critical equipment on oil & gas rigs, electrical substations, nuclear facilities, and solar and wind farms
• Unmanned data collection, scanning and quality inspection of job sites
• Remote construction site and progress monitoring
• Autonomous patrolling and monitoring of outdoor private and secured facilities
• Environment observation, real-time image transmission and data capture
• Remote crop and soil monitoring in orchards, vineyards, and other light agricultural environments.

With Husky Observer, robotics developers can get their applications up and running quickly, without needing to worry about the low-level intricacies of autonomous navigation and sensor integration. Clearpath Robotics’ services team can also add and integrate sensors based on customer requests. 

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Mobile Industrial Robots (MiR) today at ProMat introduced MiR Insights, a cloud-based software tool that enables fleet owners to track and analyze the operations of its autonomous mobile robots (AMRs). MiR said key features include data dashboards to monitor key performance indicators (KPIs) and heatmaps that track robot activity over both time and physical locations and to visualize areas with poor WiFi coverage and potential robot traffic bottlenecks.

MiR Insights is hosted on Microsoft Azure and is used in connection with MiR Fleet, MiR’s fleet management software. MiR Insights reads data from MiR Fleet but does not interfere with fleet operations or individual robot control, or adjust fleet settings. The data interface has options for data visualization as well as an API to port raw data for use in external systems.

MiR Insights will be available to customers in Q2 2023.

“MiR Insights provides robot fleet users a much better understanding of the robots’ performance and how they interact with their physical environment,” said Walter Vahey, president, MiR. “This knowledge will enable our users to continue to improve and expand their installations more rapidly and further increase their ROI.”

In late 2022, Teradyne merged its AMR subsidiaries AutoGuide Mobile Robots and MiR. This single supplier of AMRs is called Mobile Industrial Robots and is headquartered in Odense, Denmark, where MiR has managed its global operations since its launch in 2013. Prior to the merger, MiR offered a range of AMRs capable of carrying payloads and pallets up to 3,000 lb. (1350 kg). By combining with AutoGuide, the portfolio will expand to include high-payload AMR tuggers and forklifts that will operate on the MiRFleet software.

Then in early 2023, Søren E. Nielsen stepped down as president of MiR. Nielsen stepped into the role in August 2020, taking over the role from Thomas Visti Jensen. Nielsen was previously MiR’s chief technology officer since January 2018.

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Plus One Robotics, a provider of advanced AI vision software and solutions for robotic parcel handling, announced that it has raised $50 million in Series C funding. The round was led by Scale Venture Partners, with Partner Rory O’Driscoll joining the board of directors. Top Tier Capital Partners, Tyche Partners, ROBO Global Ventures, Translink, McRock, and Pritzker Group Venture Capital also participated in the round alongside existing investors which brings the company’s total funding to date to nearly $100 million.

This $50 million funding round comes less than 2 months after Plus One Robotics laid off about 10% of its staff.

With these new funds, Plus One expects to further increase its capacity and rapidly scale deployment, as well as expand its sales and marketing efforts in North America and internationally. This expansion builds on Plus One Robotics’ existing relationships with customers in the parcel post, logistics, and general merchandise industries, serving customers that include FedEx, MSC Industrial, and many more.

The labor problem

“The labor shortage is hitting the shipping industry hard, and parcel picking is an often overlooked yet essential part of the process,” said Scale Partner Rory O’Driscoll. “By automating the parcel handling piece, Plus One Robotics is rapidly modernizing an outdated system that’s no longer sustainable. It is stepping up and leading the way in a $128 billion market, with fundamentals that prove its value.”

E-commerce has grown to represent 19% of U.S. retail sales, with approximately 20 billion parcels delivered in the U.S. in 2021. Shipping growth is expected to rise by 25% over the next five years resulting in warehouses and distribution centers not having the workforce to keep up.

On the supply side, over 80% of warehouses are manual, and with the demands placed on shipping expected to grow, there will be over 1 million more jobs to fill by 2025 despite the shrinking of available labor sources – and costs are rising. Labor costs average $25 per hour and continue to increase. This creates a perfect storm threatening the supply chain and impeding future e-commerce growth.

“The growth of e-commerce has placed tremendous pressure on shipping responsiveness and scalability that has significantly exacerbated labor and capacity issues,” said Erik Nieves, CEO and co-founder of Plus One Robotics. “Automation is key, but keeping a human in the loop is essential to running a business 24/7 with greater speed and fewer errors. With the ongoing labor shortages, I believe we’ll see an increase in the adoption of Robots-as-a-Service (RaaS) to lower capital expenditures and deploy automation on a subscription basis. This new funding will help us scale up and meet the need for these solutions.”

Key to Plus One Robotics’ effectiveness is its human-in-the-loop software. Employees, remote or on-premises, can supervise multiple robots from any location, speeding the robot’s ability to handle exceptions, and enabling 24/7 operations. Users benefit from improved sorting and picking throughput by >30% while decreasing operational costs.

Plus One said it has experienced nearly three times year-over-year growth from expanded business with existing customers and new deployments. Additionally, it has increased its adoption of the human-in-the-loop capability and RaaS offering among its parcel and post, third-party logistics (3PL), and general merchandise customers.

Plus One said its deployments perform over one million parcel picks each day in production.

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John Deere has acquired SparkAI, a New York-based startup that develops human-in-the-loop technology to help robots resolve edge cases in real-time. John Deere, which has developed autonomous tractors, was a SparkAI customer for a few years prior to the acquisition. The financial terms of the deal were not disclosed.

Willy Pell, VP of autonomy and new ventures at Blue River Technology, a company John Deere acquired in 2017 for $305 million, announced the acquisition on LinkedIn. Michael Kohen, founder and CEO of SparkAI, also posted about the acquisition on the social media platform. SparkAI was founded in 2020 and had raised $7.3 million before being scooped up by John Deere.

Kohen was a guest on The Robot Report Podcast in September 2022 and discussed the company’s human-in-the-loop technology at length.

Operating in a harsh environment like a farm with dust, rain and snow can occasionally make it difficult for machine learning models to consistently make confident & safe decisions. Here’s how the technology works with John Deere’s autonomous tractors:

In moments of low confidence, the autonomous tractor automatically calls SparkAI’s service, passing imagery and other metadata via REST API. The objective is to resolve difficult-to-discern details about the scene to support a real-time decision. SparkAI said it does this by combining two key components in real-time:

• 1. Cognitive input from multiple human mission specialists trained for the use case
• 2. Results from its proprietary software-based decision system

SparkAI returns this resolution to the autonomous tractor. The robot combines this resolution with its pre-existing knowledge of the world to decide on a safe and confident action. According to SparkAI, the entire round-trip process takes seconds and happens thousands of times per day, 24/7.

SparkAI’s role in the workflow is not to take over control and remotely pilot the autonomous tractor. SparkAI provides contextual cues that the autonomous tractor is sometimes missing in order to make confident decisions.

“Spark is going to expedite everything we do. We’re going to be able to ship products sooner and at higher quality with less work,” said Pell. “When we automate away the chunks of human intervention, we will do so with great datasets that characterize the problem across many situations and environments. And all the while we will be delivering value to the customers and learning more about our technology and the environment. And then we will apply that cognition to the next area of growth.”

This is the latest in a string of robotics-related moves by John Deere. In May 2022, it acquired numerous patents and other intellectual property from Light, which specializes in depth sensing and camera-based perception for autonomous vehicles. Financial terms of the deal are unknown. Light was founded in 2013 and raised $185.7 million before the acquisition.

In April 2022, John Deere formed a joint venture with GUSS Automation, a Kingsburg, California-based developer of semi-autonomous orchard and vineyard sprayers. Through the joint venture, Deere will help GUSS further collaborate with the Deere sales channel.

In August 2021, John Deere acquired Bear Flag Robotics for $250 million, a Calif.-based developer of autonomous driving technology for tractors. Founded in 2017, Bear Flag Robotics retrofits its autonomy stack onto existing tractors. It uses cameras, LiDAR and radar technology for redundant, 360-degree situational awareness on a farm.

And, of course, John Deere’s quest for autonomy was kicked off by its acquisition of Blue River Technology in 2017 for $305 million.

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On the show today, Steve Crowe and Mike Oitzman discuss breaking news about the uncloaking of Figure.AI, a new humanoid robot company with a mission to create an affordable and ubiquitous humanoid robot.

They also discuss the features of the latest ChatGPT release, which delivers scalable infrastructure to support large enterprise interaction with ChatGPT via API. This formalizes the opportunity for roboticists to programmatically interact with ChatGPT for robotic applications. What will you do with it?

Our featured guest his week is Dejan Pangercic, co-founder and CTO of APEX.AI. Dejan talks about software development for autonomous vehicles and how APEX.AI built a hardened operating system for autonomous vehicles around ROS 2. We discuss uses for APEX.AI in both on-road vehicles and for agriculture.

Episode timeline:

06:37  ChatGPT for roboticists

11:24  Uncloaking of Figure.ai

32:20 Autonomous vehicle news: Waymo starts fully driverless testing in LA, Argo.AI relaunched as Latitude.AI. Tesla FDS recall.

40:02 Interview with Dejan Pangercic

Robotics Summit & Expo (May 10-11) returns to Boston

Links from the show today:

• Argo AI team joins Ford’s new self-driving startup Latitude AI
• OpenAI releases APIs for production use of ChatGPT
• Figure.ai website
• Figure Promises First General-Purpose Humanoid Robot (IEEE Spectrum)
• Today, Figure emerges from stealth. (LinkedIn)
• APEX.AI Grace
• APEX.AI IDA

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