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What are the challenges of autonomous intelligent systems? Why can robots and humans learn so much from each other when playing table tennis and air field hockey? And what should newcomers to the AI sciences look out for? Prof. Dr. Jan Peters explains in an expert interview. Since March 2022, he has been Head of Research for Systemic AI for Learning Robots at the new DFKI Lab in Darmstadt.
You have been a professor at TU Darmstadt for some time and are now also head of research at DFKI. What is the focus of your research and what fascinates you and your team the most?
Technically speaking, in the Research Department "Systems AI for Robot Learning – SAIRLOL", the focus is primarily on basic research into Machine Learning for intelligent autonomous robot systems. In other words, the development of methods and corresponding architectures for such systems. But also their application in Cognitive Science, with biologically and neuronally inspired approaches to Artificial Intelligence, for example interaction via brain-robot interfaces or for robot-assisted rehabilitation and prosthetics. We are particularly fascinated by the playful aspects of learning robots, from which many lessons can be learned. For example, we are currently trying to teach robots to play air field hockey. Before that, we worked on table tennis for almost ten years.
Why table tennis and air field hockey?
We wanted to show that a robot learning system can learn such a game from a human – and do it better and faster than a classically programmed robot. That's why we started with table tennis. Because until now, no one has been able to build or program a table tennis robot that is comparable to a human. We have managed to do that. But there are other hurdles in table tennis. The intelligence aspect is now fading into the background. A much bigger challenge is the great mechanics of humans. Muscles can quickly produce high accelerations that robots generally find difficult ¬– even if robots are more accurate and faster. While we have succeeded in developing a robot with human-like accelerations, this already brings us to the next difficulty: humans can move one step to the side and perform the same stroke again there. In the case of a robot, this means that it would have to be placed on a mobile base. This requires enormously complex hardware and is therefore difficult to implement. A stationary robot arm is therefore not only limited in its mobility, but also has to learn many different strokes in its position. Nevertheless, table tennis is a comparatively regular situation. When played according to the rules, for example, the ball must come up in play on the opponent's side of the table tennis.
In air field hockey the situation is incomparably more exciting. Unlike table tennis, it is downright chaotic. For example, if the puck is hit in a minimally different way, it can go to a completely different spot and a whole new response to the shot must be found. Humans are also good at tricking in air field hockey, for example by hinting at wrong stroke directions as feints. The robot has to react to this as well. In our research prototype, we have two robotic arms playing against each other and use other robots to assist, for example to re-throw the puck. We are looking at all levels of what kind of intelligence is needed for the game. On the one hand, it requires motor intelligence. Furthermore, it requires perceptual, perception-based input and more long-term strategy. One needs to predictively mesh the robot and build models about the opponent. In this way, it can be intelligently tricked and a more exciting gameplay occurs. These are different cognitive levels to be on, and we can draw a lot of insights from this about how they interact, as well as between neural and symbolic AI.
"A huge potential exists in industrial robotics if we no longer had to adapt the environment to the robot."
What potential do you see for learning robots in the future? How can the field develop further?
At the moment, robot application is based on us manually adapting the environment to the robots. Industrial robot environments have been developed in such a way that hardly any sensor needs to be used. The machines travel the same trajectories with 150 micrometer accuracy. If a human enters the workspace, it can be life-threatening for them. That's why robots are often in cages there, and great care is taken to keep people away from them. But in industrial robotics, there is huge potential if we no longer had to adapt the environment to the robot. A former CEO of the mechanical engineering company Kuka once put it succinctly: "The robots of today perform the same movement millions of times. The robots of the future will have to perform thousands of different movements just a few times." This will probably make up the customer-centric products of the future, but it is not feasible with classic robot programming. To get it right, robotics will have to change fundamentally: No longer should the environment be adapted to the robot, but the robot should learn to adapt to tasks and the environment. This is precisely what the field of "robot learning" is concerned with.
The potential of adaptive robots is probably not limited to industrial applications, is it?
In fact, it goes beyond that. For example, pick-up and drop-off tasks in clinics could be taken over by robots. That would give nurses more time to provide care. No job would be endangered by this – on the contrary. It would make work easier and more pleasant for many. One can also take a look at rehabilitation. This area should be supported by adaptive devices. Comparable to the systematics in air field hockey, where it is a matter of creating models about the opponent, here one can build models about the patient in order to support him appropriately in his movements. We have already developed an example of this with the Tübingen Clinic and the Max Planck Institute for Intelligent Systems. We were able to show that the combination of brain and computer works via corresponding brain interfaces with a robot arm and robot learning. This makes it possible to provide motor support in rehabilitation.
"The robot vacuum cleaner has already proven that robots will enter millions of homes if they are affordable."
Can you give a preview of what the household robot will look like in the near future?
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>>Read the full interview on DFKI.de!
Press contact:
DFKI Corporate Communications
Email: uk-kl@dfki.de
https://www.dfki.de/web/news/jan-peters-interview Read the complete interview on DFKI.de
Prof. Dr. Jan Peters
Jürgen Mai
DFKI
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Elektrotechnik, Gesellschaft, Informationstechnik, Maschinenbau, Wirtschaft
überregional
Buntes aus der Wissenschaft, Forschungsprojekte
Englisch
Prof. Dr. Jan Peters
Jürgen Mai
DFKI
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