A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements

Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community, as it significantly enhances robot's autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner's search space to prune the task planner's exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements.

An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning

One popular technique to solve temporal planning problems consists in decoupling the causal decisions, demanding them to heuristic search, from temporal decisions, demanding them to a simple temporal network (STN) solver. In this architecture, one needs to check the consistency of a series of STNs that are related one another, therefore having methods to incrementally re-use previous computations and that avoid expensive memory duplication is of paramount importance. In this paper, we describe in detail how STNs are used in temporal planning, we identify a clear interface to support this use-case and we present an efficient data-structure implementing this interface that is both time- and memory-efficient. We show that our data structure, called \deltastn, is superior to other state-of-the-art approaches on temporal planning sequences of problems.

Robust Plan Execution with Unexpected Observations

In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which committing to a wrong ordering of actions can cause the plan failure, even when all the actions succeed. We propose an approach to the execution of a task plan that permits some adaptability to unexpected observations of the state while maintaining the validity of the plan through online reasoning. Our approach computes an adaptable, partially-ordered plan from a given totally-ordered plan. The partially-ordered plan is adaptable in that it can exploit beneficial differences between the world and what was expected. The approach is general in that it can be used with any task planner that produces either a totally or a partially-ordered plan. We propose a plan execution algorithm that computes online the complete set of valid totally-ordered plans described by an adaptable partially-ordered plan together with the probability of success for each of them. This set is then used to choose the next action to execute.

Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans

One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the observed reality and the formal model is encountered during execution. Robustness Envelopes characterize the set of possible contingencies that a plan is able to address without re-planning, but their exact computation is extremely expensive; furthermore, general robustness envelopes are not amenable for efficient execution. In this paper, we present a novel, anytime algorithm to approximate Robustness Envelopes, making them scalable and executable. This is proven by an experimental analysis showing the efficiency of the algorithm, and by a concrete case study where the execution of robustness envelopes significantly reduces the number of re-plannings.

Temporal Planning with Intermediate Conditions and Effects

Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are needed in the model. One limitation of several action-based planning systems is that actions are modeled as intervals having conditions and effects only at the extremes and as invariants, but no conditions nor effects can be specified at arbitrary points or sub-intervals. In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. We experimentally demonstrate that our approach is far better than standard encodings in PDDL 2.1 and is competitive with other approaches that can (directly or indirectly) represent intermediate action conditions or effects.