Building a Robotic Colleague With Personality

Anxieties about whether machines will take our jobs will soon be a thing of the past. Robots are already here, adding new dimensions to the way we live and function, and researchers are exploring how to create intelligent machines that work better with us as opposed to taking our place. Guy Hoffman, assistant professor and Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University in Ithaca, New York, is studying how a working robot’s behavior can influence its human colleagues. The robots he designs lean forward to show they are listening to human interlocutors, and when they hear music, they nod in response to the beat. Hoffman’s work indicates that subtle changes in a robot’s actions have a positive effect on the humans around it. MIT Sloan Management Review spoke with him about his research to probe what his findings imply for managing human-robot teams.

MIT Sloan Management Review: Why do robots need to understand human body language and guess our intentions?

Hoffman: Robots have traditionally been designed to carry out preprogrammed behaviors. But increasingly, researchers in my field are thinking about modeling human intentions and taking human needs into account. In the past, a robot would perform a fixed action and the human had to adapt to it, but now we want the robot and the human to adapt mutually to each other. For this to happen, the robot has to solve a lot of really hard problems that for us are almost intuitive, which is to guess what we’re trying to do or what personality type we have or which mood we might be in. When we encounter people at work, we very quickly make judgments about their personalities and change our behavior accordingly. Having a robot able to do this is crucial if it’s to become a similarly good team member.

What part do emotions play in human-robot interactions?

Hoffman: Robots have the capacity to affect our behavior emotionally in that they’re using a physical body, they’re sharing space with us, they’re moving in our surroundings. What I’ve been looking at is how robots use their bodies to express their intentions, to express what they’re trying to do, and therefore affect people emotionally. We’ve found that when a robot uses human body language, it enables the people interacting with it both to be more effective in what they’re doing and to enjoy the interaction and gain psychological benefits from it. I believe that body language and the way that we think with our bodies and through our bodies is the fastest way to our hearts.

So, how would these benefits play out in the workplace?

Hoffman: In one study, I looked at robots that solely behaved like robotic tools and the interactions they had compared with robots that were more socially expressive. In the group with the more traditional robots, participants told the machines what to do, and they did it. In the other group, the robots would start moving before they’d been told what to do, and they’d start to help even before they were sure what the person wanted. People who worked with this second group of robots got into a kind of a dance, a back and forth — everybody was moving at the same time and getting things done, even though the robot was taking more chances and would sometimes make mistakes. The results showed that people felt this robot was a better team member and had more commitment to the joint activity. When participants just ordered the robot around, they felt it was lazy; it didn’t take the initiative and wasn’t a good team member or committed to the team.

In a later study, we had robots listen to people’s stories. In that situation, participants weren’t working with the robot but were using it to get something off their chest. We specifically chose stories that were negative or traumatic, and the robot would nod at the right moment or lean forward to show that it listened and understood. The participants liked this robot more than one that seemed distracted or didn’t react at all. They thought it was smarter. Afterwards they even felt more confident about themselves when going into a stressful task. This showed that people can reap psychological benefits when a robot uses its body even in a very, very small way to show empathy.

And a third project was a musical collaboration in which a pianist and a robot played a piece together. In one case, the robot just played; it didn’t exhibit any social expression. In the other, the robot joined the music socially by nodding its head and moving to the beat, looking at the pianist and then back, looking down when it was focused and then up when it was ready for more information. When we asked people to rate the music, they thought it sounded better when the robot used social behaviors than when it acted more mechanically. They thought the musicians were on the same page, as more of a duo than two separate layers. This shows us that body language is not just the icing on the cake but actually changes the taste of the cake. And the same sorts of benefits hold for robots’ cooperation and companionship with humans.

Human behavior is so complex. How do you decide how robots should act out?

Hoffman: The way I think about it is very inspired by the arts, from my experience studying theater and playing jazz. Actors have developed tricks for turning what is essentially a very schematic and structured activity into one that appears natural and spontaneous. On stage, good actors look very natural; it looks as though the lights are on in the character’s brain. One thing they do is begin a movement before they know where it’s going to end. It’s called the impulse versus the cue, when they go to speak before their line occurs.

And then there’s improvisation, something I looked at in the musical domain, but I feel like it has its place anywhere. I think robots that could improvise at your fast-food restaurant chain would be more fluent and therefore better robotic team members — which will in turn make them more acceptable to the people working with them.

You describe effective human-robot teams as having what you call “collaborative fluency.” Is that what you’re talking about here?

Hoffman: When I started looking at robots that could anticipate what you wanted to do, I focused on robot-human teams that were building simulated cars together. A surprising finding that emerged was that even though people felt that this sort of robot was much better and smarter at doing a task, it took the team the same amount of time to finish the task. (Though in some of our research, they actually worked more quickly — it depends on the task.)

That’s when I came up with the concept of collaborative fluency. What was different about the interaction was that there was a stronger sense of teamwork, a sense that everybody was doing their part and committed to the same ends.

Think about how you interact with Siri or Google Voice or Alexa or the latest intelligent agent: It’s very much a back and forth, almost like a chess game, with one move following another. “Can I do this?” I get a response. But if you and I are talking about something we’re engaged in, if we’re a team that’s brainstorming about something, that’s not how our conversation goes. You interrupt me, I interrupt you, we build on each other, we complete each other’s sentences.

Collaborative fluency occurs when you have this sense of two or more people just rising together like a great football team or a world-class ballet. It’s almost like one mind moving together. It’s a very subjective feeling, but we’re trying to deconstruct it into a mathematical computational model. I believe this is going to be the difference between robots that are going to be a joy to work with and robots that will be annoying to work with and just make you feel as though you have [just] another job.

What advantages would this sort of robot hold for businesses?

Hoffman: I would imagine that companies are interested in their employees’ well-being. It would probably also have tangible outcomes for retention and turnover.

If we’re building technology that interacts with people, we should think about human values and the well-being of the people working with these robots. In the end, we’re building technology to improve our lives. There’s no point in just making the world incredibly efficient and depressing.

There’s a lot of anxiety about the roles robots will play in our workplaces. Presumably having more agreeable robots will make the shift an easier one?

Hoffman: Right. Obviously, robots are going to replace people in some cases — it would be naïve to think that’s not part of the story — but in many cases, and we’re already seeing this, robots and humans are working together. I was at a Ford automotive plant recently and saw robots and people producing the same cars, and in my view, we will soon see this in a lot of settings. In addition, robots will be able to use data more effectively and make independent decisions so that a lot of the lower-level decision-making can be done autonomously, and only the higher-level decisions transferred to human workers. We can see this already in collaborative surgery, where the robot may stitch up a wound but doesn’t need to be told exactly what stitch to use and how to space it.

We also see more robots coming into retail right now — offering customers promotions, for instance. They’re not only going to be facing customers, though; they’re also going to be working with human salespeople and human stock-workers. I believe that we’ll see this in the restaurant business and in fast-food restaurants, too, with robots working alongside human kitchen workers.

In all these places, we want to create a situation that is beneficial to the people working with the robots by designing robots that support their psychological well-being. I believe that the way these robots interact socially and communicate is going to be a key factor to make this more a utopian and less a dystopian future.


MIT Sloan Management Review

Robotic playing card dealer made with cardboard

Ruben, aka “Ruubz0r,” a mechanical engineering student, was tasked with building a smart object. As he enjoys card games, he decided to make a playing card distributor.

The resulting device uses a single servo to slide cards off of a deck, along with a stepper motor and ultrasonic sensor to aim it at the human recipient. An Arduino Uno provides the brains of the operation.

The system is made out of wood and cardboard, and while it may not be ready for casino use, it’s a great example of what can be done with readily available materials. Check it out in action in the video seen here!

Arduino Blog

Exotec Solutions unveils ‘3D’ robotic retail order system

exotec skypod robots for logistics and retail e-commerce

French start-up Exotec Solutions is working with e-commerce giant Cdiscount to test a scalable, speedy autonomous robot network that could take order fulfilment to the next level. 

Ferrying items from one side of a warehouse to another is well within the operating parameters of many industrial robots. In fact, from online retailers such as Amazon to groceries from Ocado, the brute-force side of the order fulfilment process is increasingly dependant on nuts and bolts, AI and autonomous machines.

However, the final steps of picking, placing and packaging require a level of dexterity that only humans are capable of – although that too is changing. Ocado’s robots are getting smarter and events like Amazon’s Picking Challenge are enabling researchers to test out new techniques in real-world scenarios.

For now though, with the orders mounting up, online retailers are keen to speed up the process in any way possible.

Read more: Ocado’s robots offer ‘safe pair of hands’ for packing shopping

Taking order fulfilment to new heights

French AI robotics start-up Exotec Solutions is today launching Skypod Robots, an autonomous system designed to improve order processing speed and, according to the company, work two times faster than its rivals.

French e-commerce company Cdiscount has been testing the Skypods at its Bordeaux warehouse and seen order processing speed rise by a factor of four. The Skypod system is making an impact for three reasons.

First is the ability of the ‘3D mobile robots’ to scale shelves as well as roll around at ground level. Second is speed. Scuttling about at 10 mph, the robots can quickly transfer goods in the warehouse to human operators who handle packing and shipping. Third, according to Exotec, the robots’ laser scanner navigation system allows them to travel anywhere in the storage area, while carrying boxes weighing more than 60 pounds.

Read more: Walmart testing autonomous shelf-scanning robots

Exotec emphasizes flexible deployment

Romain Moulin, CEO of Exotec Solutions, suggests that a flexible, scalable system is best positioned to disrupt e-commerce logistics. “To respond to today’s market requirements, companies are putting the emphasis on deployment speed and flexible deployment capability rather than heavy fixed infrastructure in order to best respond to rapid fluctuations in demand,” he said.

“The Skypod addresses the needs of a new generation of customers who are looking for high performance and high-density systems that can be modified every two years.”

“From inception, the system has been designed to ensure fast deployment and full scalability. Skypod’s free navigation allows the robots to travel anywhere within the system, something the competition can’t offer today,” said Renaud Heitz, Exotec’s CTO and co-founder.

“The system’s software is powered by the latest artificial intelligence, allowing us to deploy on site within days instead of weeks.”

In December last year, Exotec raised $ 3.5million from 360 Capital Partners and Breega Capital.

Read more: Ecommerce giant Alibaba opens ‘China’s smartest warehouse’

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Internet of Business

Five Robotic Process Automation Risks to Avoid

Software robots have emerged as a potential way for organizations to achieve clear cost savings. As an evolution of automated application testing and quality assurance (QA) platforms, software bots can simulate activities that humans perform via screens and applications. A software bot can be trained to perform QA tasks, such as tapping and swiping through an app like a human would, or execute repetitive service tasks such as generating bills. It does this either by recording what humans do or via scripts that are straightforward to construct. The result is the ability to automate rote tasks that do not require complex decision-making, without the need for underlying systems changes.

Deploying bots seems deceptively simple. Robotic process automation (RPA) software vendors are actively pitching their platforms, and professional services organizations are talking up the possibilities that bots offer for cost savings with minimal project spending and limited transformational pain, which is resulting in significant corporate interest. Some forward-looking organizations are using bots and other rapid-process and data-automation tool sets to free up budget and resources to kick off large-scale reengineering programs. Others are simply using the tools to give themselves a bit of breathing room to figure out where to go next with their core platforms.

Given the push toward digital agility and away from legacy systems, it’s not surprising that organizations are executing pilots with bots across their operations. But there are five major risks to consider when designing a bot strategy.

1. If bot deployment is not standardized, bots could become another legacy albatross. The way in which business organizations are adopting bots brings to mind another application adoption: measures to address the Y2K software bug at the end of the 20th century. To deal with the time-clock change at the turn of the century, many organizations circumvented legacy limitations. Business users embraced the increasing power in Microsoft Excel and Access to create complex, business-critical applications on their desktops. But as those custom-made computing tools proliferated, so did the problems due to the lack of a strong controls framework, QA, release-management processes, and other formalized IT processes. Companies then had to spend large sums of money tracking down all their wayward tools and slowly eliminating them from critical functions.

Today’s explosion of bots threatens to repeat this pattern. In many cases, the configurations of underlying applications, networks, or data services may need to be changed to allow the bots to work effectively with them. Often, the real power of bots can be realized only alongside other technology tools. For example, a bot might extract information from several hard-to-access systems and push information into a database for use by data-transformation tools, calculators, and models. These integrations require IT involvement to properly design and deploy. Without such expertise, a script designer might simply push the data into an Excel file as a proxy database, which creates another custom-tool remediation exercise — a large number of scripts, running on a larger number of bots, without the necessary standards and monitored source code that is critical in any modern enterprise technology platform. That remediation will take budget and management focus away from badly needed investments in application modernization.

The bottom line is that the scripts that program bots are software code and should be treated as such. They need to be designed using industry-standard methodologies that focus on reuse and abstraction, and they should be versioned and properly logged so that QA processes can be executed against them. It is critical that bot implementation be tightly coordinated between business users, technology teams, and, where appropriate, third-party companies hired to write the scripts. Bots should be put into production through the same tested processes that are used for all enterprise software applications.

2. Bots might make innovation more difficult — and slower. As bots are trained to interact with Windows and browser-based applications, they will become a dependency for any change to those underlying systems. If an IT team needs to roll out an upgrade, a critical patch, or any enhancement, it will need to consider how the system change will affect the bots that interact with it. Unless handled very carefully, this will potentially slow down the process of innovation.

Unlike humans, who adapt easily to small changes in the way a specific screen works or the data contained within a dropdown menu, bot scripts may not react positively to even minor changes to a user interface. When a bot “breaks,” it has the potential to cause substantial data corruption because it won’t realize that the work it is doing is wrong and won’t know that it should stop to ask questions, as a human would. Of course, some of this risk can be mitigated by good programming, but this assumes a formal software-development methodology has been used to develop the scripts — an approach that often is not taken. Even something as innocuous as changing the internal name of a screen object in application source code as part of a production release — a piece of information that is never seen by any user — can break a bot script that relies on it.

By introducing bots into their environments, companies have potentially created a set of dependencies that are poorly documented (or not documented at all), not designed to be adaptable to change, and most likely lack traceability. This creates further barriers to changing core systems, requiring more testing and verification to ensure that no bot scripts are broken. It also complicates QA environments, as they now need to encompass both the core application and the bots that run on it.

3. Broad deployment of bots, done too quickly, can jeopardize success. The risk of taking a broad approach of bot deployment from the start is that it can consume a significant amount of an organization’s budget to develop the overall governance framework — all before the organization has really determined how to make its bot investments effective. This will limit the ability of the organization to build momentum around its automation efforts, and potentially allow small and early failures to put the entire program in jeopardy.

A better strategy is to start small, demonstrate success, and then expand the overall automation program. While it is important to strategically approach bot systems, involving process users and IT, it’s also important to learn through the first few deployments how to best analyze and optimize bot platforms. This can be done via six- to eight-week deliverables. Then the organization can build on what it has learned and start to collect accurate measurements of efficiencies and cost savings.

4. Business-process owners have no incentive to automate themselves or their staffs out of jobs. It is unreasonable to assume that the people who own a process are the right people to automate it. A key premise underlying the process-automation programs that many organizations have underway is that bots will reduce the headcount required to execute core functions. Even if using bots will clearly improve the efficiency of the process and even if staff whose jobs are replaced by the use of bots get redeployed elsewhere in the company, it is a rare operations manager who will actively work to reduce the size of his or her group. Salaries and bonuses are often tied to the number of people who work for a specific manager, creating a disincentive to trade improved productivity for fewer workers.

On the other hand, process-owner expertise is necessary to understand the scope and behavior of the process so that it can be automated properly. A better solution might be to first do a scan of multiple processes to produce a heat map that prioritizes processes, then get the process owners to describe in detail how each of their processes works. Then bring in outsiders to automate the routine.

5. Bots don’t eliminate the need for rethinking core platforms. As organizations build bot strategies and tactical plans, they need to keep in mind the hammer-and-nail analogy: When you give someone a shiny new hammer, suddenly every problem starts looking like a nail.

It’s true that bot platforms can help automate manual processes and improve productivity. It’s also true that there are other tools that can achieve even higher levels of productivity and cost savings, often in conjunction with bots. These tools include end-to-end process digitization, rapid process reengineering, user self-service interfaces, custom-tool remediation, and machine learning. It’s important to fill out the toolbox, so to speak, with a range of efficiency solutions and not bring down the bot hammer to fix every problem.

The technology infrastructure in many companies suffers from consistent underinvestment. While bots can free up some resources, they don’t eliminate the need for organizations to take a hard look at their IT capabilities and think about how they need to be modernized. There is a risk that the success of small automation exercises results in management concluding that it can avoid the expense and risk of larger initiatives. That isn’t the case.


MIT Sloan Management Review

Husqvarna robotic lawnmowers to collect data in city parks

Husqvarna robotic lawnmowers to collect data in city parks

Robotic lawnmowers from Husqvarna are set to collect and report real-time environmental data from parks around the world.

IoT sensor company Telit has put wireless sensors, co-developed by itself and Wireless System Integration (WSI), into robotic lawnmowers from Swedish manufacturer Husqvarna as part of the company’s city robotic mower pilot program.

In collaboration with data science and research community Quantified Planet and cities worldwide, the Husqvarna Automowers will be used at parks in Edinburgh and London in the UK, Gothenburg and Stockholm in Sweden, Almere and Leeuwarden in the Netherlands and in San Francisco in the US.

Cutting grass, collecting data

Equipped with wirelessly-connected sensors, the robotic mowers will collect data about the environment, air quality, water and levels of light and sound, in order to illustrate how robotic mowers can improve overall park maintenance.

The data will be collected by Quantified Planet, using a cellular connection and a digital cloud. All mowers, which are operated using a special smartphone app, are pin-protected and are fitted with alarms and GPS technology so that they can be disabled remotely if they are moved without authorization. To ensure public safety, sensors detect any nearby objects, including people and animals, causing machines to change direction.

The sensor box mounted on top of the mowers uses the robots’ main battery for power supply, recharging whenever the robot returns to base. The sensor box transmits the data using Telit’s HE910 cellular module and Telit’s global IoT connectivity data plans.

Read more: Greentomatocars joins IoT network mapping air pollution in London

Future park and city management

In cooperation with Husqvarna, meanwhile, Quantified Planet will receive the data and publish it for citizens to review. City authorities can then analyze that data and implement programs to improve the health of its citizens, based on their insights.

“Collecting this city data gives researchers the opportunity to explore and research the health of urban public spaces in a way that has never been done before,” said Maja Brisvall, CEO, Quantified Planet.

“By using the Quantified Planet data exploration platform, this new data can provide insights and innovation on how to develop and improve sustainable open green spaces which impact the citizens living nearby.”

Pavel Hajman, president of the Husqvarna division involved in the project, said that the need for green spaces is growing in urban areas, and said he found it “inspiring” to think about how parks in cities will be maintained in the future.

“I am excited about the pilot program, learning more about the possibility to increase sustainability and productivity in professional landscaping for urban areas,” he added.

Yosi Fait, Interim CEO of Telit, said that Husqvarna’s city mower program is an example of how cities are using IoT to become more sustainable and efficient.

“Through this collaboration we have been demonstrating again our unique sensor to cloud capabilities, cutting our customers time to market through our integrated lines of products and services as well through our professional services team’s significant IoT know-how.”

Read more: Intel and Bosch team up to monitor air pollution

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