AI News, Let’s Bring Rosie Home: 5 Challenges We Need to Solve for Home Robots

Let’s Bring Rosie Home: 5 Challenges We Need to Solve for Home Robots

While science has brought us many of the inventions dreamed of in sci-fi shows, one major human activity has remained low tech and a huge source of frustration: household chores.

It probably doesn’t come as a surprise that industrial behemoths such as GE, Westinghouse, and AEG (now Electrolux) shepherded miniature versions of the machines used in factories into suburban homes.

But before wondering when we’ll have home robots, it might be fair to ask: Do we even need them?Consider what you can already do just by tapping on your phone, thanks to a host of on-demand service startups.

I don’t think anyone has a compelling answer to that question today, and home robots will probably evolve and transform themselves over and over until they findtheir way into our homes.

Scientists are starting to tackle this by applying concepts used in programming toward establishing rules for robot-human conversations, but we’ll need much more if we want to have engaging AI assistants like the one in the movie “Her.” ​

The array of sensors the robot would need to properly perceive its environment could render it cost prohibitive unless the sensors cost pennies as they do in mobile phones.

Meanwhile, DARPA is funding the research on chip-based lidar, and Quanergy expects to launch a solid-state optical phased array, thereby eliminating the mechanical components that raise the cost of lidar.

We expect engineers to find creative ways to reduce the cost of existing sensing technology, while obviating others altogether, and hopefully making them as cheap as sensors in our phones today.

A robot like Roomba performs two tasks: running a suction motor and generating a path along what’s expected to be a flat surface with rigid obstacles.

These tasks require a suite of capabilities ranging from recognizing objects, identifying grasping points, understanding how an object will interact with other objects, and even predicting the consequences of being wrong.

DARPA, NSF, NASA, and European Union science funding agencies are sponsoring much-needed research in this area, but “solving manipulation” will probably require leveraging a numberof different technologies, including cloud robotics and deep learning.

Like the manipulation problem, real-time navigation requires robots to quickly sense, perceive, and execute—probably several orders of magnitude faster that they DRC winning team is today.

More robotics and industrial automation companies are embracing the notion of humans overseeing robots, with the expectation of going from the (superfluous) 1:1 human-robot ratio to a single operator being able to oversee/assist many robots.

Five ways robots are going mainstream

Unit sales for 2016 were expected to reach 6.69 million units, and for the next four years, a cumulative average growth rate of 26.5 percent, according to research from Technavio.

In the future, people can expect robots to help out in the operating room, for example, because they can mimic human motions—but with a far greater level of precision and without getting tired.

Robots might also come in soft versions that emulate human muscles and can take on delicate tasks, handle fragile objects, and change their shape to work in different environments.

An industrial robot has a price tag of several hundred thousand dollars, but newer, more modular, flexible, and collaborative robots are starting to hit prices under $30,000—about what you’d pay for a company car.

Think about the traditional domains of robots, such as automobile manufacturing plants, highly computerized warehouses, and steel mills—all environments where robots work well because their surroundings are tightly controlled, highly structured, and pre-engineered with great precision.

Robots are now starting to move beyond such structured environments and into more dynamic and less predictable ones, where people, objects, and even other robots work together to create a more fluid atmosphere.

And in some cases, such tasks [like removing weeds] go undone because either no one wants to do them or the business cannot justify the cost of doing them,” says Blue River vice president of business development Ben Chostner, “Skipping these tasks limits the performance the business is able to achieve.” Highly automated factory floors are so isolated that lights-out manufacturing—where operations literally can be performed in the dark—became popular in the early 2000s.

To keep human workers safe, robots work in isolation behind metal cages, and any interaction with humans—for maintenance, service, or material transfer—could pose a disruption and reduce overall productivity.

Tomorrow’s robots are getting downright cozy with humanity, to the point where a new term has been coined to describe them: Cobots, short for collaborative robots, are smaller, safer, simpler, and friendlier versions of their industrial counterparts.

For instance, variable impedance actuators help some cobots stiffen (to transfer energy to carry out a task, for example) or soften (to absorb force when coming in contact with a human or object).

For example, researchers at University of California, Berkeley, are working on algorithms that allow robots to learn new skills through trial and error, mimicking the way a human would learn a task.

“The robot uses the training database to learn the behaviors applicable to a new type of plant.” The concept of autonomy—think self-driving cars and automated drones—is heavily wrapped up in learning new behaviors.

The beginning of this trend is already evident in offerings from companies like Modbot, which has developed a flexible set of components that can be constructed à la Lego bricks to create the robot you need—and reconfigured later when those needs change.

Perhaps the most profound shift will be robots moving from the factory floor, where they have been hidden from human view, to many other points along the enterprise value chain—including the front of the business, where they will interact directly with customers and employees.

Although robots’ capabilities are increasing while costs are going down, certain businesses might not adopt robots today for many reasons, including: Given the diverse tasks and environments for which robots are becoming suitable, businesses can find many opportunities to innovate and apply the emerging capabilities.

Where machines could replace humans—and where they can’t (yet)

As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern.

Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.

Last year, we showed that currently demonstrated technologies could automate 45 percent of the activities people are paid to perform and that about 60 percent of all occupations could see 30 percent or more of their constituent activities automated, again with technologies available today.

In this article, we examine the technical feasibility, using currently demonstrated technologies, of automating three groups of occupational activities: those that are highly susceptible, less susceptible, and least susceptible to automation.

Toward the end of this article, we discuss how evolving technologies, such as natural-language generation, could change the outlook, as well as some implications for senior executives who lead increasingly automated enterprises.

In discussing automation, we refer to the potential that a given activity could be automated by adopting currently demonstrated technologies, that is to say, whether or not the automation of that activity is technically feasible.2 2.We define “currently demonstrated technologies”

Occupations in retailing, for example, involve activities such as collecting or processing data, interacting with customers, and setting up merchandise displays (which we classify as physical movement in a predictable environment).

The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it.

For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent.3 3.Emek Basker, “Change at the checkout: Tracing the impact of a process innovation,”

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment: workers carry out specific actions in well-known settings where changes are relatively easy to anticipate.

Through the adaptation and adoption of currently available technologies, we estimate the technical feasibility of automating such activities at 78 percent, the highest of our seven top-level categories (Exhibit 2).

Since predictable physical activities figure prominently in sectors such as manufacturing, food service and accommodations, and retailing, these are the most susceptible to automation based on technical considerations alone.

Within manufacturing, 90 percent of what welders, cutters, solderers, and brazers do, for example, has the technical potential for automation, but for customer-service representatives that feasibility is below 30 percent.

A service sector occupies the top spot: accommodations and food service, where almost half of all labor time involves predictable physical activities and the operation of machinery—including preparing, cooking, or serving food;

We calculate that 47 percent of a retail salesperson’s activities have the technical potential to be automated—far less than the 86 percent possible for the sector’s bookkeepers, accountants, and auditing clerks.

The heat map in Exhibit 3 highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them.4 4.For a deeper look across all sectors in the US economy, please see the data representations from McKinsey on automation and US jobs, on public.tableau.com.

Long ago, many companies automated activities such as administering procurement, processing payrolls, calculating material-resource needs, generating invoices, and using bar codes to track flows of materials.

Examples include operating a crane on a construction site, providing medical care as a first responder, collecting trash in public areas, setting up classroom materials and equipment, and making beds in hotel rooms.

Already, some activities in less predictable settings in farming and construction (such as evaluating the quality of crops, measuring materials, or translating blueprints into work requirements) are more susceptible to automation.

The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).

For now, computers do an excellent job with very well-defined activities, such as optimizing trucking routes, but humans still need to determine the proper goals, interpret results, or provide commonsense checks for solutions.

Overall, healthcare has a technical potential for automation of about 36 percent, but the potential is lower for health professionals whose daily activities require expertise and direct contact with patients.

One of the biggest technological breakthroughs would come if machines were to develop an understanding of natural language on par with median human performance—that is, if computers gained the ability to recognize the concepts in everyday communication between people.

The actual level will reflect the interplay of the technical potential, the benefits and costs (or the business case), the supply-and-demand dynamics of labor, and various regulatory and social factors related to acceptability.

E-commerce players, for example, compete with traditional retailers by using both physical automation (such as robots in warehouses) and the automation of knowledge work (including algorithms that alert shoppers to items they may want to buy).

The greater challenges are the workforce and organizational changes that leaders will have to put in place as automation upends entire business processes, as well as the culture of organizations, which must learn to view automation as a reliable productivity lever.

Understanding the activities that are most susceptible to automation from a technical perspective could provide a unique opportunity to rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects.6 6.See Aaron De Smet, Susan Lund, and William Schaninger, “Organizing for the future,”

McKinsey Quarterly, January 2016., It could also inspire top managers to think about how many of their own activities could be better and more efficiently executed by machines, freeing up executive time to focus on the core competencies that no robot or algorithm can replace—as yet.

Robot companions are coming into our homes – so how human should theybe?

Although many of these robots show some form of initiative and encourage people to interact with them, many are responsive rather than active – in other words, the robot waits for a human request before acting.

Although the popular and controversial Turing test is used in AI to measure whether a machine is as intelligent as a human, it is a very different thing when it comes to robots, since robots are also expected to act intelligently.

However, all robotic researchers seem to agree that the robot would have to be able to show some social awareness and personality, and be capable of understanding and recognising people’s speech and expressions.

If intelligent vacuum cleaners were able to differentiate a sleeping human from objects, for example, at least one unfortunate lady in South Korea wouldn’t have had her hair “eaten” by her new domestic appliance.

Although AI has been able to perform certain tasks extremely skillfully, for example Alpha Go, the community is still a long way from developing an AI which closely resembles the human mind.

Jibo, for example, is a social robot that can talk, order food, remind you of things, or take pictures, while Roomba is an intelligent, but ultimately functional, vacuum cleaner.

Sense of control and Robot anxiety in Human Robot Interaction, showed that the more controlling and anxious about robots a person is, the more initiative they expect the robot to show and the more willing they are to delegate tasks to it.

Participants could choose between manually turning on the cleaning robot themselves, having their robot companion turn on the cleaning robot remotely when instructed, or having the robot companion turn on the cleaning robot when it noticed that cleaning needed to be done.

This paradoxical result may be explained by the fact that people are now more used to technology – from computers and smartphones to smartwatches and intelligent home appliances – acting semi-autonomously.

When Will We Have Robots To Help With Household Chores?

Many people are excited about this and believe that this will greatly accelerate robotics technology development and hopefully make robots ubiquitous in our lives.

In a simplistic sense, the home robot market can be divided into three categories: (1) robots helping with dull and tedious household chores;

A robot working outside the home will need to be able to deal with a wide variety of weather conditions and safety issues.

Based on preliminary estimates, I expect people will be willing to pay $200 to $500 per month to rent a robotic assistant for the home.

So if a robot can save 40 hours of tedious chores per month, then people will be willing to pay $200 to $400 per month for the robot.

In order for robots to be able to do 40 hours’ worth of useful chores in homes, a lot of new technology will need to be developed.

We will need to meet or exceed 40 hours per month of useful robotic chores at home to create a significant home market and associated infrastructure.

In my opinion, realizing a home robot is technologically feasible (as shown in the images below), but it will require billions of dollars of investment in technology development to ensure the high level of reliability and safety needed for home use.

Cell phones were invented for people to talk, but they have found new roles such as music players, Web browsers, email clients, cameras, gaming devices, and so forth.

Initially people will be interested in getting robots at home to help with specifics household chores, but soon they will find new uses for these robots.

Moreover, I believe that robots might find an easier path to become popular in people’s homes by adopting new roles such as personal trainers, entertainers, and tutors (say, music instructor, golf instructor).

If some of the tech giants mentioned above—or any other company flush with cash—decide to goafter this technology, then we might have these robots before the end of this decade.

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