PDDL 2.1 is the community standard for specifications of temporal planning problems, involving actions that have a duration and can overlap in time. Recent work has shown that some modelling features, such as intermediate and conditional effects, can be expressed in PDDL 2.1 by means of specific encodings. At the core of these encodings is a construction that requires two events to happen simultaneously. However, in practice, almost none of the state-space heuristic search planners known in the literature are capable of finding plans exhibiting this required simultaneity, suggesting that the search approach they use is actually incomplete with regards to the official PDDL 2.1 semantics. In this paper, we explore this issue both theoretically and experimentally. On the theoretical side, we define two different notions of required simultaneity, and we isolate which features of the semantics of PDDL 2.1 allow for such behaviors and how to possibly change the semantics to forbid each of them. In particular, we prove that the crucial detail is how the over-all conditions interact with the mutex relation. From these observations we isolate the reason why most search-based planners cannot find plans with required simultaneity, and provide an updated search strategy that recovers semantic completeness at the cost of a larger branching factor which, however, can be suitably pruned thanks to an application of our results. On the experimental side, we compare the proposed search strategies, showing that our pruning criterion allows us to recover semantic completeness without significant overhead.

One of the major techniques to tackle temporal planning problems is heuristic search augmented with a symbolic representation of time in the states. Augmenting the problem with composite actions (macro-actions) is a simple and powerful approach to create "shortcuts" in the search space, at the cost of augmenting the branching factor of the problem and thus the expansion time of a heuristic search planner. Hence, it is of paramount importance to select the right macro-actions and minimize the number of such actions to optimize the planner performance. In this paper, we first discuss a simple, yet powerful, model similar to macro-actions for the case of temporal planning, and we call these macro-events. Then, we present a novel ranking function to extract and select a suitable set of macro-events from a dataset of valid plans. In our ranking approach, we consider an estimation of the hypothetical search space for a blind search including a candidate set of macro-events under four different exploitation schemata. Finally, we experimentally demonstrate that the proposed approach yields a substantial performance improvement for a state-of-the-art temporal planner.

Integrating metric time into Task And Motion Planning (TAMP) is challenging, especially with simultaneous object motion. Existing work focuses on classical and numeric TAMP, not considering deadlines, motions overlapping in time, and other temporal constraints. In this paper, we fill this gap by formalizing Temporal Task and Motion Planning (TTAMP) for multi-object navigation. We propose a novel interleaved planning technique for this problem, which leverages incremental Satisfiability Modulo Theory to ensure efficient reasoning on deadlines and action duration coupled with a motion planner supporting simultaneous object motion. Geometric data on encountered obstacles prunes unreachable symbolic regions, while temporal bounds limit the geometric search space. For multiple moving objects, our algorithm contextualizes the conflicts learned from the motion planner on overlapping actions so that entire classes of temporal plans are pruned from the search space of the task planner, ensuring the eventual termination of the interplay. We provide a comprehensive benchmark suite and demonstrate the effectiveness of our solver in leveraging these scenarios.

Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given. The idea is to extract a heuristic from the value function of a particular (possibly infinite-state) MDP constructed over the training problems. In this paper, we propose an evolution of this learning and planning framework that focuses on exploiting the information provided by symbolic heuristics during both the RL and planning phases. First, we formalize different reward schemata for the synthesis and use symbolic heuristics to mitigate the problems caused by the truncation of episodes needed to deal with the potentially infinite MDP. Second, we propose learning a residual of an existing symbolic heuristic, which is a “correction” of the heuristic value, instead of eagerly learning the whole heuristic from scratch. Finally, we use the learned heuristic in combination with a symbolic heuristic using a multiple-queue planning approach to balance systematic search with imperfect learned information. We experimentally compare all the approaches, highlighting their strengths and weaknesses and significantly advancing the state of the art for this planning and learning schema.

Learning domain knowledge from small training problems to improve planning performance on arbitrarily sized problems is a highly active research area. Many works explored the use of macro-actions to create “shortcuts” in the search space, at the cost of increasing the branching factor of the problem. In temporal planning, a recent technique proposes to equip a heuristic-search temporal planner with selected “macro-events”: a “shortcut” mechanism similar to macro-actions but with state-dependent semantics. In this paper, we generalize macro-events to a lifted representation, making them independent of specific problem objects. We devise a fully automated framework that, given a domain and a collection of small training problems, constructs and selects a suitable set of lifted macro-events. We define a learning pipeline that mixes the optimization of the statistical expectation on an abstraction of the problem with an empirical refinement of the selection on a validation set. We experimentally show that the proposed approach scales to complex problems, yielding substantial improvements over the baseline.

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.

Automated temporal planning is the problem of synthesizing, starting from a model of a system, a course of actions to achieve a desired goal when temporal constraints, such as deadlines, are present in the problem. Despite considerable successes in the literature, scalability is still a severe limitation for existing planners, especially when confronted with real-world, industrial scenarios. In this paper, we aim at exploiting recent advances in reinforcement learning, for the synthesis of heuristics for temporal planning. Starting from a set of problems of interest for a specific domain, we use a customized reinforcement learning algorithm to construct a value function that is able to estimate the expected reward for as many problems as possible. We use a reward schema that captures the semantics of the temporal planning problem and we show how the value function can be transformed in a planning heuristic for a semi-symbolic heuristic search exploration of the planning model. We show on two case-studies how this method can widen the reach of current temporal planners with encouraging results.

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.