We are currently seeking a motivated and talented master’s student to investigate and improve upon a developed method that learns cost and constraints explicitly as part of their master’s thesis.

In this master’s thesis, the main task is to design a method to predict changes in the state of the environment after a push action. The environment consists of constrained, cluttered space containing rigid objects.

In this project, we develop a shared control framework that guarantees safety using control-invariant sets (CISs), which are computed from the robot’s dynamics and an environmental model. The CISs ensure that unsafe human commands are overridden, while safe commands are executed normally.

We are currently seeking for a motivated and talented master’s student to work on discriminative filtering and feature extraction for classification of tactile signals.