This paper presents a framework to define a task with freedom and variability in its goal state. A robot could use this to observe the execution of a task and target a different goal from the observed one; a goal that is still compatible with the task description but would be easier for the robot to execute. We define the model of an environment state and an environment variation, and present experiments on how to interactively create the variation from a single task demonstration and how to use this variation to create an execution plan for bringing any environment into the goal state.

Abstract--We propose a method to systematically represent both the static and the dynamic components of environments, i.e. objects and agents, as well as the changes that are happening in the environment, i.e. the actions and skills performed by agents. Our approach, the Concept Hierarchy, provides the necessary information for autonomous systems to represent environment states, perform action modeling and recognition, and plan the execution of tasks. Additionally, the hierarchical structure supports generalization and knowledge transfer to environments. We rigorously define tasks, actions, skills, and affordances that Figure 1: "How to transform the left environment into the right one?" enable human-understandable action and skill recognition. The knowledge in the Concept Hierarchy enables household robots to represent environments and to create a plan to execute tasks. Furthermore, there is no clear distinction between a task, an action, and a skill.