Transferring learned control-knowledge between planners

As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several techniques for automatically acquiring those heuristics. Usually, a planner solves a problem, and a ML technique generates knowledge from the search episode in terms of complete plans (macro-operators or cases), or heuristics (also named control knowledge in planning). In this paper, we present a novel way of generating planning heuristics: we learn heuristics in one planner and transfer them to another planner. This approach is based on the fact that different planners employ different search bias. We want to extract knowledge from the search performed by one planner and use the learned knowledge on another planner that uses a different search bias. The goal is to improve the efficiency of the second planner by capturing regularities of the domain that it would not capture by itself due to its bias. We employ a deductive learning method (EBL) that is able to automatically acquire control knowledge by generating bounded explanations of the problem-solving episodes in a Graphplan-based planner. Then, we transform the learned knowledge so that it can be used by a bidirectional planner.20th International Joint Conferences on Artificial Intelligence (IJCAI-07)Hyderabad, India, 9 - 12 Jan 2007Publicad

Repeatable Motion Planning for Redundant Robots over Cyclic Tasks

We consider the problem of repeatable motion planning for redundant robotic systems performing cyclic tasks in the presence of obstacles. For this open problem, we present a control-based randomized planner, which produces closed collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. The proposed algorithm, which relies on bidirectional search and loop closure in the task-constrained configuration space, is shown to be probabilistically complete. A modified version of the planner is also devised for the case in which configuration-space paths are required to be smooth. Finally, we present planning results in various scenarios involving both free-flying and nonholonomic robots to show the effectiveness of the proposed method

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

From perception to action and vice versa: a new architecture showing how perception and action can modulate each other simultaneously

Presentado en: 6th European Conference on Mobile Robots (ECMR) Sep 25-27, 2013 Barcelona, SpainArtificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. From the Automated Planning point of view, a relevant area can be seen as an area where the objects involved in the execution of a plan are located. Thus, the planning system should guide the attention model to track relevant objects. But, at the same time, the perceived objects may constrain or provide new information that could suggest the modification of a current plan. Therefore, a plan that is being executed should be adapted or recomputed taking into account actual information perceived from the world. In this work, we introduce an architecture that creates a symbiosis between the planning and the attention modules of a robotic system, linking visual features with high level behaviours. The architecture is based on the interaction of an oversubscription planner, that produces plans constrained by the information perceived from the vision system, and an object-based attention system, able to focus on the relevant objects of the plan being executed.Spanish MINECO projects TIN2008-06196, TIN2012-38079-C03-03 and TIN2012-38079-C03-02. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

A Single-Query Manipulation Planner

In manipulation tasks, a robot interacts with movable object(s). The configuration space in manipulation planning is thus the Cartesian product of the configuration space of the robot with those of the movable objects. It is the complex structure of such a "Composite Configuration Space" that makes manipulation planning particularly challenging. Previous works approximate the connectivity of the Composite Configuration Space by means of discretization or by creating random roadmaps. Such approaches involve an extensive pre-processing phase, which furthermore has to be re-done each time the environment changes. In this paper, we propose a high-level Grasp-Placement Table similar to that proposed by Tournassoud et al. (1987), but which does not require any discretization or heavy pre-processing. The table captures the potential connectivity of the Composite Configuration Space while being specific only to the movable object: in particular, it does not require to be re-computed when the environment changes. During the query phase, the table is used to guide a tree-based planner that explores the space systematically. Our simulations and experiments show that the proposed method enables improvements in both running time and trajectory quality as compared to existing approaches.Comment: 8 pages, 7 figures, 1 tabl