We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: First, we simplify the search over plans by leveraging knowledge of skill objectives; second, shorter plans are generated by enforcing aggressively hierarchical planning; and third, we learn transition dynamics with sparse local models for better generalization. Our framework decomposes transition dynamics into skill effects and success conditions, which allows fast planning by reasoning on effects, while learning conditions from interactions with the world. We propose a simple method for learning new abstract skills, using successful trajectories stemming from completing the goals of a curriculum. Learned skills are then refined to leverage other abstract skills and enhance subsequent planning. We show that both conditions and abstract skills can be learned simultaneously while planning, even in stochastic domains. Our method is validated in experiments of increasing complexity, with up to @math states, showing superior planning to classic non-hierarchical planners or reinforcement learning methods. Applicability to real-world problems is demonstrated in a simulation-to-real transfer experiment on a robotic manipulator.
 We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world n, del as an arbitrary collection of first-order predicate calculus formulas and is designed to work with .models consisting of large numbers of formulas. It employs a resolution theorem prover to answer questions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model. A compact representation of all possible assembly plans of a given product using AND OR graphs is presented. Such a representation forms the basis for efficient planning algorithms that make possible an increase in assembly system flexibility by allowing an intelligent robot to pick a course of action according to instantaneous conditions. The AND OR graph is equivalent to a state transition graph but requires fewer nodes and simplifies the search for feasible plans. Three applications are discussed: the preselection of the best assembly plan, the recovery from execution errors, and the opportunistic scheduling of tasks. A hypothetical error situation in the assembly of the four-part assembly is discussed to show how a bottom-up search of the AND OR graph leads to an efficient recovery. The scheduling efficiency using this representation is compared with fixed sequence and precedence graph representations. > We present an approach to coordinate the behavior of a multiagent team using an HTN planning procedure. To coordinate teams, high level tasks have to be broken down into subtasks which is a basic operation in HTN planners. We are using planners in each of the agents to incorporate domain knowledge and to make agents follow a specified team strategy. With our approach, agents coordinate deliberatively and still maintain a high degree of reactivity. In our implementation for use in RoboCup Simulation League, first results were already very promising. Using a planner leads to better separation of agent code and expert knowledge. SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system with the following characteristics. • SHOP plans for tasks in the same order that they will later be executed. This avoids some goal-interaction issues that arise in other HTN planners, so that the planning algorithm is relatively simple. • Since SHOP knows the complete world-state at each step of the planning process, it can use highly expressive domain representations. For example, it can do planning problems that require complex numeric computations. • In our tests, SHOP was several orders of magnitude faster man Blackbox and several times faster than TLpian, even though SHOP is coded in Lisp and the other planners are coded in C. In this letter, we propose a framework for task allocation in human-robot collaborative assembly planning. Our framework distinguishes between two main layers of abstraction and allocation. In the higher layer, we use an abstract world model, incorporating a multiagent human-robot team approach in order to describe the collaborative assembly planning problem. From this, nominal co-ordinated skill sequences for every agent are produced. In order to be able to treat humans and robots as agents of the same form, we move relevant differences peculiarities into distinct cost functions. The layer beneath handles the concrete skill execution. On atomic level, skills are composed of complex hierarchical and concurrent hybrid state machines, which in turn co-ordinate the real-time behavior of the robot. Their careful design allows to cope with unpredictable events also on decisional level without having to explicitly plan for them, instead one may rely also on manually designed skills. Such events are likely to happen in dynamic and potentially partially known environments, which is especially true in case of human presence.