Intelligent Robots Operating in the Real World: From Agriculture Robots to Autonomous Cars

Autonomous robots need the ability to perceive and model their environment and make appropriate decisions in complex situations. The complexity results from the high-dimensional perceptions, the large number of possible actions, and the uncertainty about the state of the world. In this talk, I will present recent developments for solving relevant problems in different application areas. The talk covers results from two research directions: agricultural robotics and self-driving cars. The first part of my talk tackles robots in agriculture. Crop farming is essential in our society, providing us with food, feed, fiber, and fuel. We heavily rely on agricultural production, but at the same time, we need to reduce the footprint of agriculture production. Agricultural robots and other new technologies offer promising directions to address key management challenges in agricultural fields. To achieve this, autonomous field robots need the ability to perceive and model their environment, predict possible future developments, and make appropriate decisions in complex and changing situations. Related problems also appear in other application areas such as autonomous cars. Also in this domain, autonomous vehicles need geometric and semantic models of the world. In the second part of my talk, I will present recent developments using supervised and unsupervised learning for the perception system of autonomous cars.

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