Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the realworld have to contend with a great deal of uncertainty, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent, a direct side effect of partial observability induced by sensor limitations and occlusions.

Regardless of the source, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It will not pause while the robot computes a solution. Autonomous robots navigating among people, for example in traffic, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice, with potentially catastrophic consequences when something unexpected happens.

The aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for realworld robots. We explore a range of methods, from probabilistic to deep learning, as well as different combinations with optimization-based methods from robotics, planning and control. Driven by applications in robot navigation, and grounded in experiments with real autonomous quadcopters, we address several parts of this problem. From reducing uncertainty by learning better models, to directly approximating the decision problem itself, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy.


This work has been supported by the Wallenberg AI, Autonomous Systems and Software Program, the Swedish Foundation for Strategic Research (SSF) project Symbicloud and the ELLIIT Excellence Center at Linköping-Lund for Information Technology, in addition to those sources already acknowledged in the individual papers.

View all events
We use cookies to personalise content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners. View more
Cookies settings
Privacy & Cookie policy
Privacy & Cookies policy
Cookie name Active
The WASP website wasp-sweden.org uses cookies. Cookies are small text files that are stored on a visitor’s computer and can be used to follow the visitor’s actions on the website. There are two types of cookie:
  • permanent cookies, which remain on a visitor’s computer for a certain, pre-determined duration,
  • session cookies, which are stored temporarily in the computer memory during the period under which a visitor views the website. Session cookies disappear when the visitor closes the web browser.
Permanent cookies are used to store any personal settings that are used. If you do not want cookies to be used, you can switch them off in the security settings of the web browser. It is also possible to set the security of the web browser such that the computer asks you each time a website wants to store a cookie on your computer. The web browser can also delete previously stored cookies: the help function for the web browser contains more information about this. The Swedish Post and Telecom Authority is the supervisory authority in this field. It provides further information about cookies on its website, www.pts.se.
Save settings
Cookies settings