Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge

Autonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system...

Knowledge-based Approach for Mobile Manipulation with Active Inference

Achieving human-like action planning requires profound reasoning and context-awareness capabilities. It is especially true for autonomous robotic mobile manipulation in dynamic environments. In the case of component failure, the autonomous robotic system requires reliable adaptation capabilities combined with a consistent understanding of the...

Multi-­Robot Exploration and Planning in Limited Communication Environments

Distributed cooperative robots can be highly beneficial in mapping disaster environments and assisting with search and rescue operations. In most situations such environments only allow for only limited communication between robots. This thesis reports on simulation experiments conducted to test the impact of having only partial communication...