Welcome to the

Adaptive and Interpretable Learning Systems Lab

About the Lab

The AILS lab conducts fundamental research in Machine Learning and Artificial Intelligence. Our goals include improving the data-efficiency of learning algorithms, enabling knowledge transfer between machine learning models, reasoning under uncertainty, and adapting models to various types of dataset shift.

The AILS lab is part of the Centre for Applied Autonomous Sensor Systems (AASS) at Örebro University. Furhter, the AILS lab is closely affiliated with the WASP research program.


01.10.2022 - Multiple publications

The AILS lab members published multiple papers at IROS 2022.
The conference was held in Kyoto, Japan. This made for a great Japan trip for almost the entire group.
Read our papers here.

01.09.2022 - Lab launch 🎉

The AILS lab has been created in September 2022.

Latest Publication

A Stack-of-Tasks Approach Combined With Behavior Trees : A New Framework for Robot Control

Dominguez, David Caceres and Iannotta, Marco and Stork, Johannes Andreas and Schaffernicht, Erik and Stoyanov, Todor

Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.