Extrapolating Robots Learn To React To Changing Physical Situations
Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation. But how do you know what will break a robot without actually damaging it?
A robot needs to learn about his body and the environment. It tries a few different motions and uses the algorithm. It can then predict what will happen with larger movements and at higher speeds.
Credit: IST Austria/Birgit Rieger
The key feature of the new method is that it strives to reveal the true dynamics of the situation: it takes in data and returns the equations that describe the underlying physics. “If you know those equations,” says Georg Martius, “then you can say what will happen in all situations, even if you haven’t seen them.” In other words, this is what allows the method to extrapolate reliably, making it unique among machine learning methods.
The team’s method sets itself apart in several other ways as well. First, the final approximations previously produced during machine learning were far too complex for a human to understand or work with. In the new method, the resulting equations are far simpler: “Our method’s equations are something you would see in a textbook–simple and intuitive,” says Christoph Lampert. The latter is another key difference: other machine learning methods give no insight into the relationship between conditions and results–and thus, no intuition on whether the model is even plausible.
And it’s not all theory: “In my group, we’re actually working on developing a robot that uses this type of learning. In the future, the robot would experiment with different motions, then be able to use machine learning to uncover the equations that govern its body and movement, allowing it to avoid dangerous actions or situations,” adds Martius. While robots are one active area of research, the method can be used with any type of data, from biological systems to X-ray transition energies, and can also be incorporated into larger machine learning networks.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 291734.
The research received funding from the ISTFELLOW program, a Marie Sk?odowska-Curie COFUND grant co-funded by IST Austria and the European Union through the Horizon 2020 research and innovation programme. This program has since been succeeded by another COFUND grant, the ISTplus program, which is open for applications from qualified postdocs all over the world.
Contacts and sources:
Elisabeth Guggenberger
The Institute of Science and Technology (IST Austria)
Citation: S. S. Sahoo, C. H. Lampert, and G. Martius. Learning equations for extrapolation and control. In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018. PMLR, 2018. to appear
Arxiv Preprint: https://arxiv.org/abs/1806.07259
Link to conference: https://icml.cc/
Source: http://www.ineffableisland.com/2018/07/extrapolating-robots-learn-to-react-to.html
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