Category Robotics Classification

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Technology and robots will shake labour policies in Asia and the world

Developing countries must begin seriously considering how technological changes will impact labour trends. KC Jan/Shutterstock

By Asit K. Biswas, University of Glasgow and Kris Hartley, The Education University of Hong Kong

In the 21st century, governments cannot ignore how changes in technology will affect employment and political stability.

The automation of work – principally through robotics, artificial intelligence (AI) and the Internet of things (IoT), collectively known as the Fourth Industrial Revolution – will provide an unprecedented boost to productivity and profit. It will also threaten the stability of low- and mid-skilled jobs in many developing and middle-income countries.

From labour to automation

Developing countries must begin seriously considering how technological changes will impact labour trends. Technology now looms just as large a disruptive force, if not larger, than the whims of global capital.

China has for decades increased its global contribution to manufacturing value-added goods, now enjoying a competitive position in Apple products, household appliances, and technology. In the process, the country has made historic progress lifting its citizens out of poverty.

China has accomplished this by raising worker productivity through technology and up-skilling (improving or acquiring new skills), and higher wages have predictably followed.

However, this trend is also compelling manufacturers to relocate some low-skill production to Southeast Asia. US-China trade disputes could exacerbate this trend.

Relocation of manufacturing activity has been an economic boon for workers in countries like Vietnam and Indonesia. However, the race among global manufacturers to procure the cheapest labour brings no assurances of long-term growth and prosperity to any one country.

Governments in developing countries must parlay the proceeds of ephemeral labour cost advantages into infrastructure investment, industrial upgrading and worker upskilling. China has done this to better effect than many.

The growth in sophistication and commercial feasibility of robotics, IoT, and other automation technologies will impact jobs at nearly every skill level. More broadly, the fallout from technological advancement may replicate the disruptive geographic shifts in production once resulting from labour cost arbitrage.

Political blowback

After many decades of globalisation, a borderless economy has emerged in which capital and production move freely to locations with the greatest investment returns and lowest cost structures. This has prompted a pattern of global economic restructuring, generating unprecedented growth opportunities for developing countries.

Workers have been rewarded for their personal efforts in education and skill development, while millions have been lifted from poverty.

Given advancements in technology and the associated impact on livelihoods, it is time to consider how the next chapter of global development will play out politically. Automation will be a highly disruptive force by most economic, social, and political measures. Few countries – developed or otherwise – will escape this challenge.

Some Western countries, including the United States, are already experiencing a populist political wave fuelled in part by the economic grievances of workers displaced from once stable, middle-class manufacturing jobs. Similar push-back may erupt in countries already embroiled in nationalist politics, including India.

Growing populations and the automation of work will soon mix to create unemployment crises, with serious implications for domestic political stability.

As education systems flood the employment market with scores of ambitious graduates, one of the greatest challenges governments face is how to generate well-paying jobs.

Further, vulnerable workers will include not only new entrants but also experienced workers, some of whom are continuously and aggressively up-skilling in anticipation of more lucrative employment.

In India, over 1 million people enter the working-age population every month. More than 8 million new jobs are needed each year to maintain current employment levels.

India’s young population is becoming increasingly pessimistic about their employment prospects. Although official statistics are unreliable, as a large percentage of work occurs in the informal sector in positions such domestic workers, coolies, street vendors, and transient positions lacking contracts, indications are that India may be facing the prospect of jobless growth.

Insufficient skill levels in much of the workforce are impeding India’s effort to accelerate growth in high-productivity jobs. Thus, the country’s large-scale manufacturers, both domestically and internationally owned, are turning to robots to ensure consistent, reliable, and efficient production.

Urbanisation also adds to India’s employment challenge. The promise of higher-paying jobs has lured many rural workers into urban areas, but these workers are often illiterate and lack sufficient skills. This was not always a concern, as these workers could find menial factory jobs. Robots are now doing much of the low-skilled work that migrant workers were once hired to do.

Towards a future of stable livelihoods

The lingering socio-economic imperative for many governments is to replace eliminated jobs. According to The World Economic Forum, “inequality represents the greatest societal concern associated with the Fourth Industrial Revolution.”

However, the WEF and others have given little useful guidance on how to address this challenge. How should the economy absorb multitudes of variously skilled workers displaced by technology?

People aspire to economic and social mobility more than ever before, particularly as they observe wealth rising ostentatiously all around them – on the streets, in the news, and among seemingly lucky friends and acquaintances. Sadly, the aspirations of most will go unfulfilled.

One way forward is said to be through up-skilling by retraining workers to operate and maintain technology systems. However, this seems to be a paradox, as workers would be training robots to eventually take jobs held by humans. If a major driver of automation is reduction or elimination of labour costs, one cannot expect all displaced workers to enjoy stable and continuing employment opportunities.

Despite political promises about employment growth from high-tech industries and the technological transformation of primary sectors, the tension between the drive for technology-based efficiency and the loss of jobs is undeniable and may have no clear resolution.

Societies have reacted to global economic restructuring in discouraging ways, indulging in nationalism, racism, militarism, and arbitrary economic protectionism. Populist opportunists and foul-tempered troglodytes have ridden reactionary rhetoric into positions of political power, raging against what former White House chief strategist Steve Bannon calls the “liberal postwar international order.” At the same time, left-leaning solutions such as universal basic income face significant fiscal and political headwinds.

The 21st century will see increased disruptions to once-stable work life, due to technological progress and the continuing liberalisation of global capital and production. Early indications about how countries will respond – haphazardly and with no clear long-term strategy – are not encouraging.The Conversation

Asit K. Biswas, Visiting professor, University of Glasgow and Kris Hartley, Assistant professor, The Education University of Hong Kong

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The first tendril-like soft robot able to climb

Researchers at IIT-Istituto Italiano di Tecnologia created the first soft robot mimicking plant tendrils. It is able to curl and climb using the same physical principles determining water transport in plants. The research team is led by Barbara Mazzolai, and results have been published in Nature Communications. In the future, this tendril-like soft robot could inspire the development of wearable devices such as soft braces that actively morph their shape.

Interactive control to guide industrial robots

Scientists at the Fraunhofer Institute for Machine Tools and Forming Technology IWU have developed an innovative technology enabling people and large industrial robots to work together in an intuitive way that feels a lot like human teamwork. Using the benefit of this technology, robots can recognize gestures, faces and postures to make this collaboration that much safer and more efficient. Fraunhofer IWU is set to present this innovation at the Hannover Messe Preview in hall 19 on January 24, 2019, and at the Hannover Messe in hall 17 at booth C24 from April 1 through 5, 2019.

#278: IROS 2018 Exhibition (Part 3 of 3), with Ryan Gariepy, Lars Grimstad and Péter Fankhauser


In this interview, Audrow Nash interviews Ryan Gariepy, Lars Grimstad, and Péter Fankhauser.

Ryan Gariepy, Chief Technology Officer of ClearPath and Otto Motors in Canada, speaks about Boxer, a robust research platform that has been used extensively in an industrial context over many years. Gariepy discusses how Boxer can be used in investigating human-robot interaction questions because of its expressive lighting, including for autonomous cars.

Lars Grimstad, Chief Technology Office of Saga Robotics in Norway, discusses a lego-like agriculture platform. He speaks about the platform’s design, including its power systems and communication protocol. Grimstad also talks about a project using this platform to pick hanging strawberries.

Péter Fankhauser, Chief Business Development Officer and Co-founder of ANYbotics in Switzerland, speaks about ANYmal, a quadrupedal robot. Fankhauer discusses going from research to industry, using ANYmal for inspection, and the future of ANYbotics.

 

Links

Robots are being programmed to adapt in real time

In trials, the ResiBot robot learned to walk again in less than two minutes after one of its legs was removed. Image credit – Antoine Cully / Sorbonne University

By Gareth Willmer

It’s part of a field of work that is building machines that can provide real-time help using only limited data as input. Standard machine-learning algorithms often need to process thousands of possibilities before deciding on a solution, which may be impractical in pressurised scenarios where fast adaptation is critical.

After Japan’s Fukushima nuclear disaster in 2011, for example, robots were sent into the power plant to clear up radioactive debris in conditions far too dangerous for humans. The problem, says robotics researcher Professor Jean-Baptiste Mouret is that the robots kept breaking down or came across hazards that stopped them in their tracks.

As part of the ResiBots initiative, he is designing a lower-cost robot that can last long periods without needing constant human maintenance for breakages and are better at overcoming unexpected obstacles.

The ResiBots team is using what it refers to as micro-data learning algorithms, which can help robots adapt in front of one’s eyes in a similar way to how animals react to problems. An animal will, for example, often find a way to continue moving if they get injured, even if they don’t know exactly what the problem is.

In contrast, most current robots self-diagnose a problem before working out a way to overcome it, says Prof. Mouret, principal investigator at ResiBots and a senior researcher at the Inria research centre in France.

‘We’re trying to shortcut this by finding a way for them to react without necessarily having developed an understanding of what’s wrong,’ he said.

Rather than self-diagnosing, the aim for these robots is to learn in a proactive way by trial and error what alternative actions they can take. This could help them overcome difficulties and stop them from shutting down in situations such as disaster scenarios like Fukushima, said Prof. Mouret.

This may not be full artificial intelligence, but Prof. Mouret points out that having knowledge of everything is not essential for getting a robot to work.

‘We’re not trying to solve everything,’ he said. ‘I’m more interested in how they can adapt – and, in fact, adapting to what’s happening is some of what makes animals intelligent.’

Simulated childhood

In one of the most promising approaches developed in the ResiBots project, the robots have a simulated childhood, in which they learn different ways to move their body using an algorithm that searches ahead of time to collect examples of useful behaviours. 

This means that when seeking a way to move, the robots need to choose from one of about 13,000 behaviours rather than an estimated 1047 options that standard algorithms could select from. And the aim is for them to try only a handful of these before finding one that works.

Most of ResiBot’s tests are currently being carried out on a six-legged robot that seeks to find new ways to move after having one or more legs removed. In the latest trials, Prof. Mouret said the robots learned to walk in one to two minutes after one of their legs was taken off, meaning they generally need to test fewer than 10 behaviours before finding one that works.

Test robots can learn to overcome a broken leg in under two minutes. Video credit – Horizon

In total, the researchers are working on half a dozen robots at varying levels of complexity including a child-like humanoid robot known as iCub. Though the much more complex iCub is not yet being used in many trials, the team hopes to do so more over time.

‘Humanoids have the potential of being highly versatile and adapting well to environments designed for humans,’ said Prof. Mouret. ‘For instance, nuclear power plants have doors, levers and ladders that were designed for people.’

There are, however, some big challenges still to overcome, including the fact that a robot needs to be moved back to its starting position once a limb is removed, rather than being able to carry on from the injury site towards the target.

Safety

There are also wider safety issues involving such robots – for example, ensuring that they do not harm earthquake survivors while rescuing them, particularly if the robot is learning by trial and error, said Prof. Mouret.

He believes it will be at least four or five years before such a robot could be used in the field, but is hopeful that the techniques can eventually be employed in all types of robot – not just those for disaster situations, but in the home and other scenarios.

But it’s not just mechanics that can help robots navigate the real world. Robots may also adapt better if they can more strongly connect language to reality. 

Professor Gemma Boleda at the Universitat Pompeu Fabra in Spain, has a background in linguistics and her team is trying to link research in this field to artificial intelligence to help machines better understand the world around them, as part of a project called AMORE.

It’s something that could be useful for making technology such as GPS more intelligent. For example, when driving in a car, the GPS system could specify that you turn right where ‘the big tree’ is, distinguishing it from several other trees.

Prof. Boleda says this has been hard to do in the past because of the difficulty of modelling the way humans link language with reality.

‘In the past, language had largely been represented out of context,’ said Prof. Boleda.

AMORE’s aim is to get computers to understand words and concepts in a real-world context rather than as individual words in isolation, she says. For instance, a robot would learn to connect the phrase ‘this dog’ with an actual dog in the room, representing both the words and the real-world entities.

‘The crux of these models is that they are able to learn their own representations from data,’ she added. ‘Before, researchers had to tell the machine what the world looked like.’

Giving machines a better understanding of the world around them will help them do ‘more with less’ in terms of the amount of data they need and get better at predicting outcomes, Prof. Boleda said.

It could also help with the issue of having enough physical space on devices like mobile phones for the next wave of intelligent applications.

‘I am working with language, but this problem of needing a lot of data is a problem that plagues many other domains of artificial intelligence,’ said Prof. Boleda. ‘So if I develop methods that can do more with less, then these can also be applied elsewhere.’

The research in this article was funded by the EU’s European Research Council.

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