  The need for autonomous robot systems in both the service and the industrial
domain is larger than ever. In the latter, the transition to small batches or
even "batch size 1" in production created a need for robot control system
architectures that can provide the required flexibility. Such architectures
must not only have a sufficient knowledge integration framework. It must also
support autonomous mission execution and allow for interchangeability and
interoperability between different tasks and robot systems. We introduce
SkiROS2, a skill-based robot control platform on top of ROS. SkiROS2 proposes a
layered, hybrid control structure for automated task planning, and reactive
execution, supported by a knowledge base for reasoning about the world state
and entities. The scheduling formulation builds on the extended behavior tree
model that merges task-level planning and execution. This allows for a high
degree of modularity and a fast reaction to changes in the environment. The
skill formulation based on pre-, hold- and post-conditions allows to organize
robot programs and to compose diverse skills reaching from perception to
low-level control and the incorporation of external tools. We relate SkiROS2 to
the field and outline three example use cases that cover task planning,
reasoning, multisensory input, integration in a manufacturing execution system
and reinforcement learning.
