Sebastian Stock

TL;DR: The architecture and knowledge-representation framework for a service robot being developed in the EU project RACE is described, and examples illustrating how learning from experiences will be achieved are presented.

TL;DR: The planner CHIMP is introduced, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space.

TL;DR: The general system architecture is introduced and some results in detail regarding hybrid reasoning and planning used in RACE are sketches, and instances of learning from the experiences of real robot task execution are sketched.

TL;DR: It is shown that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable.

TL;DR: The straightforward integration of different kinds of knowledge for causal, temporal and resource knowledge as well as knowledge provided by an external path planner is demonstrated, resulting in an HTN planner able to handle very rich domain knowledge.