Swiss researchers visit a watch-making school, to teach robots think like a craftsman.
Could robots put Swiss watchmakers out of business? Not for a long time. In fact, robots really struggle to emulate the kind of delicate, fine-tuned manipulation to be found in industries like watchmaking.
How to close the dexterity gap between humans and robots is the subject of a research project, funded by the European Research Council and led by Aude Billard, a professor at the Swiss Federal Institute of Technology in Lausanne. “We roboticists need to understand how humans learn to do tasks that go beyond your natural sense of touch,” says Billard. “In the case of watchmaking, humans manage to control micro-level displacements and forces with precision, which is challenging given that the human sensory system is incredibly noisy,” she adds, referring to the continual input we get from our senses. The questions puzzling Billard are: How do humans learn to place their arms, fingers and joints and constrain them to overcome this sensory-motor noise? How do they manage to model the world in which they are working?
One hypothesis is that we simplify. For instance, we may learn to forbid our joints to move in a certain way, only allowing the tactile impact to happen in specific directions. And we appear to filter our sensory inputs, focusing on what’s important for the task at hand. “Skilled watchmakers dedicate about 80 per cent of the time spent on a task just placing the finger correctly,” she explains. “Doing the task itself is very quick. The same is true of catching an object. Preparation is important.”, states Billard.
Her aim is not to disrupt her country’s most famous industry.” We are not interested in teaching robots how to make watches,” Billard stresses. “We are most interested in how robots can learn to implement that kind of process and which variables you need to learn to control precisely.”
Billard’s research team is also developing software that can enable robots to interact with moving objects, so they can, for example, learn to catch items by achieving the right orientation. The ultimate goal is to create robots that can learn many different tasks, rather than being hard-wired to perform a specific job. “We want to reach a point where robots have a flexible brain, so that when they get out of the factory, they learn whatever task you want them to do,” Billard says.
Aude Billard is a professor and head of the Learning Algorithms and Systems Laboratory at the School of Engineering of Switzerland’s École Polytechnique Fédérale de Lausanne.
This post is part of collaboration between EuroScientist and ERC=Science². ERC=Science² is a pan-European communication campaign using popular scientific themes such as “senses” and “artificial intelligence” to highlight the scientific research funded by the European Research Council and the potential impact it can have on society.