Robot Democratization: A Machine for Every Manufacturer   


With collaborative robots proliferating, we wanted to know who’s using these robots and what tasks they’re doing. Design News caught up with Walter Vahey, executive vice-president at Teradyne, a company that helps manufacturers gear up their automation. Vahey sees a real change in the companies that are deploying robotics. For years robots were tools only for the largest manufacturers. They required expensive care and feeding in the form of integrators and programming. Now, collaborative robots require configuration rather than programming, and they can be quickly switched from task to task.

Vahey talked about robot companies such as Universal Robots (UR) which produces robot arms, and MiR, a company that produces collaborative mobile robots. He explained how they’re putting robotics in the hands of smaller manufacturers that previously could not afford advanced automation. The difference is that these robots are less expensive, they can be set up for production without programming, and they can be quickly reconfigured to change tasks.

Universal Robots, MiR, Taradyne, robotics, robots, automation, small manufacturers
Robots are now within the investment reach of small manufacturers. That's fueling a surge in the use of collaborative robots. (Image source: Universal Robots)

We asked Vahey what’s different about collaborative robots and what he’s seeing in robot adoption among smaller manufacturers.

Design News: Tell us about the new robots and how they’re getting deployed.

Walter Vahey: Companies such as Universal Robots and MiR are pioneering the robot space. They’re bringing automation to a broad class of users and democratizing automation. For small companies, the task at hand is to figure out how to fulfill their orders. It’s particularly challenging to manufacturers. In a tight labor market, manufacturers are facing more competition, growing demand, and higher expectations in quality.

Manufacturer can plug UR or MiR robots in very quickly. Everything is easy, from the specs up front to ordering to quickly arranging and training the robot. There’s no programming, and the robots have the flexibility to do a variety of applications. Every customer is dealing with labor challenges, so now they’re deploying collaborative robots to fulfill demand with high quality.

The whole paradigm has shifted now that you have a broader range of robot applications. You can easily and quickly bring in automation, plug it in ,and get product moving in hours or days rather than months. That’s what’s driving the growth at UR and MiR.

The Issue of Change Management

Design News: Is change management a hurdle?. Does the robot cause workforce disruption?

Walter Vahey: We really haven’t seen that as an issue. The overwhelming need to improve and fulfill demand at a higher quality level helps the manufacturers deploy. It outweighs other challenges. We help with the deployment, and the manufacturers are making the change easily.

We grew up as a supplier of electronic test equipment. Since 2015, we’ve entered the industrial automation market with a focus on the emerging collaborative robot space. We see that as a way to change the equation for manufacturers, making it faster and easier to deploy automation.

Design News: What about return on investment? Robotics can be a considerable investment for a small company/

Walter Vahey: The customers today are looking for relatively short ROI, and we’re seeing it from 6 months to a year. That’s a no brainer for manufacturers. They’re ready to jump in.

We work hard to make deployment less of an issue. We have an application builder, and we use it to prepare for deployment. The new user may have a pick-and-place operation. They choose the gripper, and we guide them to partners who make it easy to deploy.

The application builder helps the customer pick the gripper. The whole object is to get the customer deployed rapidly so the automation doesn’t sit. With MiR, the robot comes in, and we find an easy application for the mobile device. We take the robot around the plant and map it. We’ve work to guide customers through an application quickly and make the robot productive as soon as possible.

There are hundreds of partners that work with UR and MiR, providing grippers and end effectors. We have a system that customers can plug into. Customer can look at grippers from a wide range of companies. We’re not working just on the robot deployment. We work to get the whole system deployed so they can quickly get the ROI.

What Tasks Are the Robots Taking On?

Design News: Who in the plant is using the robots, and what tasks are involved?

Walter Vahey: There is a range of users. To be effective at training a robot and configuring it, the people best suited for it are the ones most aware of the task. To get the robot to be effective you have to know the task. By and large, the person who has been doing that task is best suited to train the robot. That person can then train other robots. Nobody’s better suited to do it than the people who know what needs to be done.

The tasks are broad set of applications. We automate virtually any task and any material movement. It’s not quite that simple, but it’s close. With UR, we’re doing machine learning, grinding, packing, pick-and-place, repetitive tasks, welding. It’s a very broad set of applications. In materials it’s also very broad. Parts going from a warehouse to a work cell, and then from the work cell to another work cell, up to a 1000-kilo payload. We’re moving robots into warehousing and logistics space, even large pieces of metal. The robots are well suited for long runs of pallets of materials.

Rob Spiegel has covered automation and control for 19 years, 17 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.

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SWN Sector Cabal Sigma Session One- The Wrong Side of Heaven (part 2)   


The four began the Danger Room "exercise" cautiously, realizing stepping on any of the squares could trigger problems, Able using his "Fix" skill disabled the panel in front of him rendering it safe to step on. Bras Hombre tried to do the same, but he was no tech. Osric took a few tentative steps into the room, only to come face to face with a robot popping up from the floor in front of him and beginning to activate it's taser hands. Osric swung his melee weapon and beheaded the robot. Varlo stepped into the room and jumped back avoiding the pit trap that opened up beneath him. Soon more robots were triggered. Then the thing...the Hybrid Mergence MOVED! It scuttled across the room quickly, triggering traps as it moved, another robot popped up and then some form of thick vision obscuring fog, then the Hybrid Mergence triggered a pit trap and fell into the trap. 
                                          The Hybrid Mergence
Able decided this was his chance and dashed across the room and dove into the pit after the thing. With each step he made a loud click occurred...multiple robots appeared, a Gatling gun popped up in the corner and whirred into life, barrels of some sort of flaming substance shot out of the wall and fire spilled out into the room approaching the edge of the pit containing Able and the thing.   
Then things went really wild, the robots that activated the prior round now moved towards the four. This trigged explosions, more robots, including a life-like robotic version of Stalin (who drew its pistol), a crevasse appeared dividing the room, and an Energy Cannon appeared. Varlo nearest to the Gatling gun, safely got to the Gatling gun (although a robot was now activated right beside Bras Hombre). Varlo hacked into the Gatling gun's system and was able to manually take control of it (he rolled so well with his skill check that he altered the things programmed damage output and rate of fire). Bras Hombre was locked in combat with a robot, while Osric used his psionic powers to telekinetically lift Able (now holding the Hybrid Mergence) out of the pit and towards the exit. Robot Stalin popped off a shot at the airborne Able hurting him but not "killing" him. Just as a flaming barrel emerged from the wall and melted robot Stalin into slag.  The Plasma gun activated, misfired and overheated blowing up. Bras Hombre had managed to pry up a floor panel giving him some cover from all the chaos. A blizzard of ice and snow(WTH?!!) activated in the middle of the room spilling over into the crevasse and mixing in with the flames from the burning barrels, steam made visibility even harder. As robots were taken out more appeared in their place. The words of the robot announcer at the start of this fiasco echoed in their heads...this was an "Introductory Scenario" to the danger room?!   
Unbeknownst to the party, their encounter was being broadcast on more channels across Riot, and even a few bars on the orbiting Outlaw Station.  Varlo then used the reprogrammed Gatling Gun to unleash a massive volley of "bullets" across the room, robots were taken out... along with Able and Bras Hombre!! In all this chaos it was friendly fire that took out half the party. The party couldn't hear it but bar patrons  cheered as Val (unintentionally?) took out two of his teammates. Osric knowing they were about to go down in flames (literally?) dashed to Able's fallen form, retrieved the Hybrid Mergence and was able to open the exit door. The program ended, the fires, fog and blizzard dispersed. The simulation was ended.  Bras Hombre and Able shook their heads, groggily waking up, bruised but alive and with bad headaches.   
The robot announcer instructed the four that the exercise was over, and directed them to the freshers and to return the Hybrid Mergence to the containment cube now appearing in the room.  Osric started in that direction, but then he sensed something. The Hybrid Mergence was a living thing! And it was projecting emotions to Osric...fear, sadness, most of all it wanted to escape this place! Osric realized it had some level of sentience and some level of psychic least some level of empathy. At the last minute Osric swapped out a small ration pack from his gear while stowing the creature in his clothing he dropped the ration pack into the containment cube, acting like he was returning the Hybrid Mergence, which now was secreted under his clothing and then excited the Danger Room. Varlo grinned from ear to ear, while Able gave him a menacing glare and Bras Hombre shook his head.


Schmidt: Computer strike zone good, but check your hearing   


A hot topic all year has been the idea of computerizing the strike zone, the rise of the robot umpires. It’s drawn even more attention here in October — we’ve already seen a perfect example of a missed call when Marcell Ozuna was rung up in the ninth inning of Game 3 between the Cardinals […]

Meet the robot racing drone that could beat human pilots by 2023 - CNET   

Teams start competing Tuesday in the Drone Racing League's new battle of autonomous drones. Here's an exclusive first look at the AI-powered flying machine.

Killing "Dead-End" Jobs Blocks Career Opportunity, by Matt Beane, Wired   


We can do better than the automation status quo. First off, I’ve shown that somewhere, we probably already are: A scant few are bending and breaking rules to keep the benefits of on-ramp jobs while adapting to work involving intelligent technologies. We need to find these people and learn from them. Beyond this, we need to take a stand. Any of us—worker, manager, technologist, policymaker—can look for ways to handle the technologies that both preserve the benefits of on-ramp jobs and deliver the productivity gains we’re all hoping for. We could create new, even-more-valuable on-ramp jobs that are possible only because of intelligent technologies, for example. Some kitchens that buy the Dishcraft system will probably realize they could get more customers by showing it off through a plexiglass wall in the hallway on the way to the conveyor where you drop your tray. They’ll need someone to keep the wall and the robot clean and answer bystander questions. That’s the beginnings of an on-ramp job, and I bet many a high-schooler would jump at the chance.


Early Estimation of User's Intention of Tele-Operation Using Object Affordance and Hand Motion in a Dual First-Person Vision. (arXiv:1910.02201v1 [cs.CV])   


Authors: Motoki Kojima, Jun Miura

This paper describes a method of estimating the intention of a user's motion in a robot tele-operation scenario. One of the issues in tele-operation is latency, which occurs due to various reasons such as a slow robot motion and a narrow communication channel. An effective way of reducing the latency is to estimate the human intention of motions and to move the robot proactively. To enable a reliable early intention estimation, we use both hand motion and object affordances in a dual first-person vision (robot and user) with an HMD. Experimental results in an object pickup scenario show the effectiveness of the method.


Action-conditioned Benchmarking of Robotic Video Prediction Models: a Comparative Study. (arXiv:1910.02564v1 [cs.CV])   


Authors: Manuel Serra Nunes, Atabak Dehban, Plinio Moreno, José Santos-Victor

A defining characteristic of intelligent systems is the ability to make action decisions based on the anticipated outcomes. Video prediction systems have been demonstrated as a solution for predicting how the future will unfold visually, and thus, many models have been proposed that are capable of predicting future frames based on a history of observed frames~(and sometimes robot actions). However, a comprehensive method for determining the fitness of different video prediction models at guiding the selection of actions is yet to be developed. Current metrics assess video prediction models based on human perception of frame quality. In contrast, we argue that if these systems are to be used to guide action, necessarily, the actions the robot performs should be encoded in the predicted frames. In this paper, we are proposing a new metric to compare different video prediction models based on this argument. More specifically, we propose an action inference system and quantitatively rank different models based on how well we can infer the robot actions from the predicted frames. Our extensive experiments show that models with high perceptual scores can perform poorly in the proposed action inference tests and thus, may not be suitable options to be used in robot planning systems.


Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping. (arXiv:1910.02646v1 [cs.RO])   


Authors: Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan Ratliff

RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensible subtask policies as Riemannian motion policies (RMPs: a motion policy and an importance matrix function), which can be a difficult design problem in its own right. We propose RMPfusion, a variation of RMPflow, to address this issue. RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment. This extra flexibility can remedy imperfect subtask RMPs provided by the user, improving the combined policy's performance. These weight functions can be learned by back-propagation. Moreover, we prove that, under mild restrictions on the weight functions, RMPfusion always yields a globally Lyapunov-stable motion policy. This implies that we can treat RMPfusion as a structured policy class in policy optimization that is guaranteed to generate stable policies, even during the immature phase of learning. We demonstrate these properties of RMPfusion in imitation learning experiments both in simulation and on a real-world robot.


Deep Learning for Robotic Mass Transport Cloaking. (arXiv:1812.04157v2 [cs.RO] UPDATED)   


Authors: Reza Khodayi-mehr, Michael M. Zavlanos

We consider the problem of Mass Transport Cloaking using mobile robots. The robots move along a predefined curve that encloses the safe zone and carry sources that collectively counteract a chemical agent released in the environment. The goal is to steer the mass flux around a desired region so that it remains unaffected by the external concentration. We formulate the problem of controlling the robot positions and release rates as a PDE-constrained optimization, where the propagation of the chemical is modeled by the Advection-Diffusion (AD) PDE. We use a Deep Neural Network (NN) to approximate the solution of the PDE. Particularly, we propose a novel loss function for the NN that utilizes the variational form of the AD-PDE and allows us to reformulate the planning problem as an unsupervised model-based learning problem. Our loss function is discretization-free and highly parallelizable. Unlike passive cloaking methods that use metamaterials to steer the mass flux, our method is the first to use mobile robots to actively control the concentration levels and create safe zones independent of environmental conditions. We demonstrate the performance of our method in simulations.


Osoyoo Model-3 Robot Learning Kit Lesson 4: Don’t Touch Me   

 Objective Parts and Devices Hardware Installation Software Installation: Ultrasonic Sensor Position Alignment Testing Authorized Online Retailers Buy from US Buy from UK Buy from DE Buy from IT Buy from FR Buy from ES Buy from JP Objective In this lesson, we will install an ultrasonic sensor on the robot car and program the ...Read the Rest

Radio - Sunday, September 29th, 2019   


Download the show here.


NICK SMASH, one of Toronto's earliest documentarians on the punk scene,is back with a look at the Club called The Edge. 40 years earlier, The Edge started booking shows thanks to the Garys. Nick has put together an issue of Smash It Up to collect stories and unseen photos on the shows that took place there. Nick presented on the edge in recent appearance in his Library series and he agreed to come in and do a supplementary piece for his Alone and Gone series on EXD.  You can get a copy of Smash It Up #40 through Nick Smash on facebook for $10.00 ppd.  Here is the playlist that we used in the airing of this interview.

MARTHA AND THE MUFFINS – Echo Beach (Dindisc)
BB GABOR – Soviet Jewellery (Anthem)
CHELSEA – Right to Work (Step Forward)
PENETRATION – Don’t Dictate (Virgin)
THE SLITS - Difficult Fun (CBS)
ULTRAVOX – He’s a Liquid (Live in Philadelphia)
THE MODS – Reactions (Ugly Pop)
XTC - Life Begins At The Hop (Virgin)
BLUE PETER – Do The Robot (Ready)
KINETIC IDEALS - Life in Shadow (Mannequin)
PROTEX - I Can't Cope (Polydor)
THE GOVERNMENT - Flat Tire (The Modern World Incorporated)
NASH THE SLASH–Dead Man's Curve (Cut Throat / Dindisc)
BATTERED WIVES - Angry Young Man (Bomb)
THE SPOONS – Nova Heart (Ready)
DOA – Nazi Training Camp (Friends)
999 - Feeling Alright with the Crew (PVC)
TYRANNA - Back Off Baby (Rave Up)

Thread: Misión Secreta:: Rules:: Discarding face up?   


by littlebrothertimmy

Why do you discard face up? In my opinion, this makez the game too easy for the good guys to spot the imposters.
If I'm an imposter and I only have cards that can be used to build, I want to discards something as well. When I discard face up, everyone can see that I'm not cooperating...

The way I see the game: pay attention to who contributed a card when the robot part can be built (when there are enough cards). This way you can get a sense of who the imposter might be when the building part failed.
I also feel that there is not much you can do to block the imposters. After all, there are only 4 block cards and they might end up in the hands of the imposters. Furthermore, the block cards might show up too late and they only slow you down. I would have preferred something like Saboteur, though I understand that the designers do not want to copy an existing game...

Process Designed for 3D-Printing Mini Soft-Robotic Actuators   


Scientists have a growing interest in the design of soft robots that are safer and more nimble than their rigid counterparts.

One area in which they’ve been challenged in the development of these robots is at the smaller scale, such as in the millimeter range, because of the complexity of fabricating such fine parts at this size.

soft robots, actuators, digital light projection, Singapore University of Technology. DLP 3D printing
Researchers in China and Singapore have combined efforts to develop a generic process flow to guide the 3D printing of miniature soft pneumatic actuators that are smaller than a coin. Researchers also designed a soft debris remover with an integrated miniature to help navigate through a confined space or collect small objects in hard-to-reach positions. (Source: Singapore University of Technology and Design)

Now a team of researchers from Singapore and China have combined efforts to develop a 3D-printing process using digital light projection (DLP) to develop pneumatic actuators for soft robots ranging in size from 2 millimeters to 15 millimeters, with a feature size of 150-350 micrometers, they said.

The method paves the way for easier fabrication of tiny soft robots well-suited for navigation in confined areas as well as the manipulation of small objects, researchers said. These robots could find use in various applications, from medical technology to jet maintenance.

Specifically, the scientists--from the Singapore University of Technology and Design (SUTD), Southern University of Science and Technology (SUSTech), and Zhejiang University (ZJU)—have presented a generic process flow for guiding DLP 3D printing of these miniature pneumatic actuators.

The method offers an alternative to the molding and soft-lithography methods that are typically used and require great delicacy, thus are more complex, said Associate Professor Qi (Kevin) Ge from SUSTech, the lead researcher of the research project.

"To ensure reliable printing fidelity and mechanical performance in the printed products, we introduced a new paradigm for systematic and efficient tailoring of the material formulation and key processing parameters,” he said in a press statement.

Multi-Step Process

DLP 3D printing is a process in which photo-absorbers are commonly added into polymer solutions to enhance printing resolutions in both horizontal and vertical directions. However, if the dose of those absorbers is too high, it can lead to rapid degradation in the material's elasticity, an aspect that’s critical for soft robots to sustain large deformations, researchers said.

To achieve their results and not sacrifice any durability in potential soft robots fabricated using the process, researchers made a number of informed decisions, said Yuan-Fang Zhang, a researchers from SUTD who worked on the project.

First the team selected a photo-absorber with good absorbance at the wavelength of the projected UV light and then conducted mechanical performance tests to determine the appropriate material formulation, he said.

“Next, we characterized the curing depth and XY fidelity to identify the suitable combination of exposure time and sliced layer thickness," Zhang said in a press statement.

This process flow enabled researchers to develop a multimaterial 3D-printing system to fabricate a variety of miniature and structurally diverse soft pneumatic robotic actuators, researchers said.

Moreover, the method should be compatible with commercial stereolithography (SLA) or DLP 3D printers without needing to make any hardware modifications, Ge said in a press statement.

Researchers published a paper on their report in the journal Advanced Materials Technologies.

To demonstrate the usefulness of their process, the team devised a soft robot as a proof-of-concept—a debris remover comprised of a continuum manipulator and a 3D-printed miniature soft pneumatic gripper, they said. The robot can navigate through a confined space as well as collect small objects in places that humans might have a difficult time reaching, researchers said.

Elizabeth Montalbano is a freelance writer who has written about technology and culture for more than 20 years. She has lived and worked as a professional journalist in Phoenix, San Francisco and New York City. In her free time she enjoys surfing, traveling, music, yoga and cooking. She currently resides in a village on the southwest coast of Portugal.

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Useless Police Robot Fails to Call For Help When Needed   


If there was an emergency and you saw a police robot patrolling the area, a reasonable person would expect that simply pushing its emergency alert button would call for help. That’s what a California woman reportedly tried to do. In reality, the robot told her to get out of the way and carried on with its business.



Keeping Up with the Bots: How the Rise of RPA Impacts IGA    


Robotic Process Automation (RPA) is a type of automation technology currently transforming the way businesses operate. RPA software robots manipulate and communicate with business systems and applications to streamline processes and reduce the burden on employees. RPA can automate tasks, including claims processing and call center support to data management, IT services, and invoice processing, and everything in between. Opportunities for automation exist virtually everywhere throughout the business, enabling greater organizational performance and efficiency.

The growth of robotic process automation is unprecedented. In fact, a recent Forrester study, highlighted in Forbes, predicted that the “RPA market will reach $1.7 billion in 2019 and $2.9 billion by 2021,” and “more than 40 percent of enterprises will create state-of-the-art digital workers by combining AI (artificial intelligence) with Robotic Process Automation.” This incredible growth suggests a tremendous shift in overall business strategy toward automating specific processes and reducing reliance on human workers for repetitive tasks that can be performed more efficiently and accurately by software bots.

report by Deloitte also suggests that “as many as 50 percent of the activities performed by a given employee are mundane, administrative, manual-labor intensive tasks,” indicating that ”RPA will replace 16 percent of jobs by 2025.“ Yet the same study indicates that only 17 percent of leaders and workforces are “ready to handle a workforce consisting of people, robots, and AI working side by side.” Clearly, RPA is changing the nature of business today. And as we advance further into automation during this century, organizations will need to change how they manage bot identities and put into place the right identity governance policies to manage their access levels within the organization. So what is the real impact of RPA on Identity Governance and Administration (IGA) and how can organizations today effectively respond to the rise of bots within their business?

Why IGA and RPA Go Hand-in-Hand

The relationship between IGA and RPA should be both mutually dependent and mutually beneficial. According to the IGA, RPA, and Managing Software Robot Identities report from Gartner, ”robotic process automation will have a profound impact on IGA. RPA introduces robotic software whose identities and access must be managed and controlled.” Further, “technical professionals must prepare to extend IGA architecture to address these requirements, while assessing RPA for automating IGA tasks.” This means that organizational IGA policies and programs must be extended to intelligently manage the identities of bots, and concurrently, RPA can aid in automating manual IGA tasks. For the remainder of this piece, we will explore the role of identity governance in managing bots within organizations today and save the discussion of robotic process automation to enhance efficiencies for IGA in a follow-up blog.  

Bots Have Identities Too

Just like the human users within an organization, non-human users, often known as service accounts or software robots, are an increasing target for attack. External threat actors have become more sophisticated in their malicious activities that target users inside the organization—whether human or robot. According to the 2019 Insider Threat Report from Cybersecurity Insiders, 70 percent of cybersecurity professionals surveyed believe that the frequency of insider attacks has increased in the last year alone. And an incredible 62 percent of organizations have experienced at least one insider attack in the past 12 months. With the increasing number of tasks that bots are now performing within organizations today, and the significant access they have to company systems, applications, and data, how can the business effectively manage their levels of access and ensure the organization is protected?

The answer is by including service accounts under the identity governance umbrella, and managing them in a similar, yet distinct way from how human users are managed. Specifically, treating service accounts as contingent workers within the organization, separate from human users, is a best practice approach for giving bots identities and managing them intelligently. Although bots act in the same way as humans, taking on the mundane, repetitive tasks of human users, categorizing them as contingent workers will clearly define the systems and applications they should and should not access. Ultimately, by extending the definition of users to incorporate bots as part of the contingent workforce, organizations can increase visibility across all their environments and more effectively protect their organization as the digital workforce continues to expand.

The User Lifecycle for Service Accounts in Robotic Process Automation

Treating bots as part of the contingent workforce begins when the service account is initiated or ‘onboarded.’ This is where the software robot receives initial account access to appropriate systems and applications. Over time, the robot may need new or different access to complete its task, so an effective IGA program must be able to manage this change. Finally, if the bot is no longer needed, accounts should be immediately disabled to avoid orphaned accounts that are prone to attack. According to Gartner, “software robot identity lifecycle management processes can be modeled to contingent workers when organizations keep software robot identities distinctly separate from people. Just as with humans, each software robot can have a supervisor or sponsor—the person who is responsible for overseeing the operation of the software robot.” By treating service accounts and software robots in a similar manner as contingent workers, organizations can more effectively manage the levels of access they have across the non-human user lifecycle, and easily onboard and offboard software robots securely and efficiently.

Embrace the Rise of Bots in Your Organization Intelligently

As companies continue to increase reliance upon robotic process automation, and depend on service accounts to increase efficiency and drive organizational performance, they must also recognize the responsibility they have in managing these bots as actual users. Identity governance for RPA will continue to play a prominent role, and it is up to organizations to leverage leading-edge IGA solutions for improving organizational security throughout the software robot user lifecycle. Make sure your organization is ready for the rise of RPA and has the proper identity governance programs in place to keep your people and your robots protected.


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