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Robotic Flexibility: How Today’s Autonomous Systems Can Be Adapted to Support Changing Operational Needs

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✍️ Author: Sara Pearson Specter

πŸ”– Topics: robotics, AI

🏭 Vertical: Machinery

🏒 Organizations: Obeta, Covariant, KNAPP

While robots are ideally suited to repetitive tasks, until now they lacked the intelligence to identify and handle tens of thousands of constantly changing products in a typical dynamic warehouse operation. That made applying robots to picking applications somewhat limited. Therefore, when German electrical supply wholesaler Obeta sought to install a new automated storage system from MHI member KNAPP in its new Berlin warehouse as a means to address a regional labor shortage made worse by COVID-19, the company specified a robotic picking system powered by onboard artificial intelligence (AI).

β€œThe Covariant Brain is a universal AI that allows robots to see, reason and act in the world around them, completing tasks too complex and varied for traditional programmed robots. Covariant’s software enables Obeta’s Pick-It-Easy Robot to adapt to new tasks on its own through trial and error, so it can handle almost any object,” explained Peter Chen, co-founder and CEO of MHI member

Read more at MHI Solutions Magazine

How the USPS Is Finding Lost Packages More Quickly Using AI Technology from Nvidia

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✍️ Author: Todd R. Weiss

πŸ”– Topics: AI, machine vision

🏒 Organizations: USPS, NVIDIA, Accenture

In one of its latest technology innovations, the USPS got AI help from Nvidia to fix a problem that has long confounded existing processes – how to better track packages that get lost within the USPS system so they can be found in hours instead of in several days. In the past, it took eight to 10 people several days to locate and recover lost packages within USPS facilities. Now it is done by one or two people in a couple hours using AI.

Read more at EnterpriseAI

Getting up to Speed: Understanding ball screw potential and limitations

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✍️ Authors: Thaniel Smith, Ian Miller

πŸ”– Topics: machine design

🏒 Organizations: Motion Industries, Thomson Industries

The ability to operate consistently at higher speeds is why motion system designers often specify ball screws over lead screws. However, ball screws have speed limitations of their own. Understanding those will help you optimize ball screw assembly performance in applications ranging from small laboratory fluid pumps to large overhead gantries and high-performance machinery.

Read more at MachineDesign

Tools Move up the Value Chain to Take the Mystery Out of Vision AI

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✍️ Author: Nitin Dahad

πŸ”– Topics: AI, machine vision, OpenVINO

🏒 Organizations: Intel, Xilinx

Intel DevCloud for the Edge and Edge Impulse offer cloud-based platforms that take most of the pain points away with easy access to the latest tools and software. While Xilinx and others have started offering complete systems-on-module with production-ready applications that can be deployed with tools at a higher level of abstraction, removing the need for some of the more specialist skills.

Read more at Embedded

Learning to Manipulate Deformable Objects

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✍️ Authors: Daniel Seita, Andy Zeng

πŸ”– Topics: robotics

🏒 Organizations: Google

In β€œLearning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks,” to appear at ICRA 2021, we release an open-source simulated benchmark, called DeformableRavens, with the goal of accelerating research into deformable object manipulation. DeformableRavens features 12 tasks that involve manipulating cables, fabrics, and bags and includes a set of model architectures for manipulating deformable objects towards desired goal configurations, specified with images. These architectures enable a robot to rearrange cables to match a target shape, to smooth a fabric to a target zone, and to insert an item in a bag. To our knowledge, this is the first simulator that includes a task in which a robot must use a bag to contain other items, which presents key challenges in enabling a robot to learn more complex relative spatial relations.

Read more at Google AI Blog