According to a report released by “swissinfo,” Switzerland is developing robots that are flexible, agile, collaborative, and intelligent, at least to the point where a gadget or machine may reach anyplace on the planet.
According to the research, Switzerland is the world champion in the field of robotics. When we look at the top twenty laboratories in the world, we see that Switzerland has over a fourth of them, despite having a population of only eight million people.
The Swiss AI pioneer and her team coexist with robots on the first floor of one of the sprawling campus’s futuristic buildings, and often create software for existing robots, according to Audi Pilar, who heads the Laboratory for Algorithms and External Linked Learning Systems at the Federal Institute of Technology in Lausanne.
Switzerland isn’t known for making robots in the first place, and the factories that do so are usually Japanese, Korean, Chinese, German, or American. However, Switzerland’s strength lies in research and the projects that result from it, particularly in new materials, programming, and artificial intelligence. And
The Swiss National Fund for Scientific Research, a public body that supports and funds research, recognized the importance of this topic early on, according to the report, and established the National Research Pole in the field of robotics in 2010, which includes six universities and higher institutes. They need to work together.
It’s time to move on to the next level after twelve years and an investment of almost $85 million. On January 1, 2022, Innosuisse, Switzerland’s official agency for the development of innovation, established the NTN for the promotion of robotics innovation, with a budget of half a million Swiss francs per year with Audi Pilar as its supervisor.
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“We will support at least ten projects a year, and accompany these start-ups as they grow, to enable them to obtain broader funding,” says the newly appointed director of the portfolio, Audrey Pilar. Heading to markets that have a greater demand for robots of various kinds, and continued, “Establishing a startup is one thing, but creating a real product is another.”
The director of the Laboratory for Algorithms and Learning Systems (LISA) asked: “In what field will these products appear? .. One of the biggest challenges for robotics is the transition from rigid structures made of metals or hard plastic to more flexible materials, because it is only the hand of the” robot. “Soft” can approach the softness and efficiency of the human hand. The issue here is not only a question of the comfort provided by the robot, but rather the interaction with the environment or with individuals and groups. She continued, “Research has come a long way, and there are certain areas that can now go into production. So I expect to see great achievements.”
She explained, “These non-rigid materials pose a new kind of problem, related to the mechanisms of control and accuracy, and to holding objects in particular. Let’s just imagine teaching a machine how to hold a carton of milk without changing the shape of the cans or dropping them.”
And Ode Pilar explains, how once we have these soft materials, we will have a non-linear motion, and then there will be a lot of uncertainty at the level of the measurements. So we’ll have to develop algorithms to control that, and that needs to use artificial intelligence. But I think robots will get to a point where this becomes possible.”
Pointing to a robotic hand that is directly controlled by the human brain, she said, “This is one of the projects of the Algorithms and Learning Systems Lab. Automated integration, which are companies that act as intermediaries between machine makers and users.
For his part, Baptiste Bosch, a technical expert, said: “We offer them solutions to change the way robots are programmed, and they are less rigid than what is currently being done, and which are the result of our own research.”
In turn, the Algorithms and Learning Systems Lab also provides users with an interface that allows the client to reprogram their robot themselves when they want to assign it to a new task. This happens in a fairly simple way, without the need for extensive expertise, and – thanks to the learning algorithms – the machine is turned on to do the job that needs to be done, without the need to reprogram it.
The report concluded that the bottom line in this research and huge scientific projects, makes working robots in the service of all categories of people, starting with the large industrial sector, and ending with the small craftsman. And in various fields.