Proceedings Article•
Planning Domain + Execution Semantics: A Way Towards Robust Execution?
01 Jan 2014-
TL;DR: It is shown that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable.
Abstract: Robots are expected to carry out complex plans in real world environments. This requires the robot to track the progress of plan execution and detect failures which may occur. Planners use very abstract world models to generate plans. Additional causal, temporal, categorical knowledge about the execution, which is not included in the planner's model, is often avail- able. Can we use this knowledge to increase robustness of execution and provide early failure detection? We propose to use a dedicated Execution Model to monitor the executed plan based on runtime observations and rich execution knowl- edge. We show that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable. A dedicated Execution model also introduces a degree of mod- ularity, since the platform- and execution-specific knowledge does not need to be encoded into the planner.
Citations
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TL;DR: Problem in different research areas related to mobile manipulation from the cognitive perspective are outlined, recently published works and the state-of-the-art approaches to address these problems are reviewed, and open problems to be solved are discussed.
Abstract: Service robots are expected to play an important role in our daily lives as our companions in home and work environments in the near future. An important requirement for fulfilling this expectation is to equip robots with skills to perform everyday manipulation tasks, the success of which is crucial for most home chores, such as cooking, cleaning, and shopping. Robots have been used successfully for manipulation tasks in wellstructured and controlled factory environments for decades. Designing skills for robots working in uncontrolled human environments raises many potential challenges in various subdisciplines, such as computer vision, automated planning, and human-robot interaction. In spite of the recent progress in these fields, there are still challenges to tackle. This article outlines problems in different research areas related to mobile manipulation from the cognitive perspective, reviews recently published works and the state-of-the-art approaches to address these problems, and discusses open problems to be solved to realize robot assistants that can be used in manipulation tasks in unstructured human environments.
43 citations
17 Dec 2015
TL;DR: The planner CHIMP is introduced, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space.
Abstract: Plan-based robot control has to consider a multitude of aspects of tasks at once, such as task dependency, time, space, and resource usage. Hybrid planning is a strategy for treating them jointly. However, by incorporating all these aspects into a hybrid planner, its search space is huge by construction. This paper introduces the planner CHIMP, which is based on meta-CSP planning to represent the hybrid plan space and uses hierarchical planning as the strategy for cutting efficiently through this space. The paper makes two contributions: First, it describes how HTN planning is integrated into meta-CSP reasoning leading to a planner that can reason about different forms of knowledge and that is fast enough to be used on a robot. Second, it demonstrates CHIMP's task merging capabilities, i.e., the unification of different tasks from different plan parts, resulting in plans that are more efficient to execute. It also allows to merge new tasks online into a plan that is being executed. This is demonstrated on a PR2 robot.
29 citations
Cites background from "Planning Domain + Execution Semanti..."
...[24], which can benefit from CHIMP’s rich plan representation....
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TL;DR: The general system architecture is introduced and some results in detail regarding hybrid reasoning and planning used in RACE are sketches, and instances of learning from the experiences of real robot task execution are sketched.
Abstract: This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.
29 citations
Cites background from "Planning Domain + Execution Semanti..."
...As pointed out in [8], the planner’s knowledge and that of the semantic execution monitor need not overlap completely: some of it may be execution-specific for improving robustness and enabling early failure detection....
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20 Oct 2014
TL;DR: The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot, and these experiences are extracted and stored in memory and they are used as input for learning methods.
Abstract: Intelligent service robots should be able to improve their knowledge from accumulated experiences through continuous interaction with the environment, and in particular with humans. A human user may guide the process of experience acquisition, teaching new concepts, or correcting insufficient or erroneous concepts through interaction. This paper reports on work towards interactive learning of objects and robot activities in an incremental and open-ended way. In particular, this paper addresses human-robot interaction and experience gathering. The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot. The human-robot interaction ontology includes not only instructor teaching activities but also robot activities to support appropriate feedback from the robot. Two simplified interfaces are implemented for the different types of instructions including the teach instruction, which triggers the robot to extract experiences. These experiences, both in the robot activity domain and in the perceptual domain, are extracted and stored in memory, and they are used as input for learning methods. The functionalities described above are completely integrated in a robot architecture, and are demonstrated in a PR2 robot.
27 citations
TL;DR: An approach for scene understanding based on qualitative descriptors, domain knowledge and logics is proposed, and promising results were obtained.
Abstract: An approach for scene understanding based on qualitative descriptors, domain knowledge and logics is proposed in this paper. Qualitative descriptors, qualitative models of shape, colour, topology and location are used for describing any object in the scene. Two kinds of domain knowledge are provided: (i) categorizations of objects according to their qualitative descriptors, and (ii) semantics for describing the affordances, mobility and other functional properties of target objects. First order logics are obtained for reasoning and scene understanding. Tests were carried out at the Interact@Cartesium scenario and promising results were obtained.
26 citations
References
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Book•
01 Jan 2006TL;DR: This chapter discusses Classical Planning and its Applications, as well as Neoclassical and Neo-Classical Techniques, and discusses search procedures and Computational Complexity.
Abstract: 1 Introduction and Overview I Classical Planning 2 Representations for Classical Planning*3 Complexity of Classical Planning*4 State-Space Planning*5 Plan-Space Planning II Neoclassical Planning 6 Planning-Graph Techniques*7 Propositional Satisfiability Techniques*8 Constraint Satisfaction Techniques III Heuristics and Control Strategies 9 Heuristics in Planning*10 Control Rules in Planning*11 Hierarchical Task Network Planning*12 Control Strategies in Deductive Planning IV Planning with Time and Resources 13 Time for Planning*14 Temporal Planning*15 Planning and Resource Scheduling V Planning under Uncertainty 16 Planning based on Markov Decision Processes*17 Planning based on Model Checking*18 Uncertainty with Neo-Classical Techniques VI Case Studies and Applications 19 Space Applications*20 Planning in Robotics*21 Planning for Manufacturability Analysis*22 Emergency Evacuation Planning *23 Planning in the Game of Bridge VII Conclusion 24 Conclusion and Other Topics VIII Appendices A Search Procedures and Computational Complexity*B First Order Logic*C Model Checking
1,612 citations
"Planning Domain + Execution Semanti..." refers background in this paper
...Planning approaches (Ghallab, Nau, and Traverso 2004) model such dependencies through preconditions and effects: preconditions specify the situation in which the robot can execute the action (e.g., being at the counter before trying to pick up the object), effects capture the expectation about the…...
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TL;DR: The SHOP2 planning system as discussed by the authors received one of the awards for distinguished performance in the 2002 International Planning Competition and described the features that enabled it to excel in the competition, especially those aspects of SHOP 2 that deal with temporal and metric planning domains.
Abstract: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.
838 citations
Proceedings Article•
01 Aug 1994TL;DR: How the complexity of HTN planning varies with various conditions on the task networks is described.
Abstract: Most practical work on AI planning systems during the last fifteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.
747 citations
"Planning Domain + Execution Semanti..." refers methods in this paper
...HTN-planners (Erol, Hendler, and Nau 1994) use a different approach....
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Proceedings Article•
11 Aug 1986TL;DR: Computing the consequences of temporal assertions is shown to be computationally intractable in the interval-based representation, but not in the point-based one, but a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.
Abstract: This paper considers computational aspects of several temporal representation languages. It investigates an interval-based representation, and a point-based one. Computing the consequences of temporal assertions is shown to be computationally intractable in the interval-based representation, but not in the point-based one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter.
746 citations
"Planning Domain + Execution Semanti..." refers methods in this paper
...In order to have tractable reasoning upon temporal constraints, we use convex IA relations (Vilain and Kautz 1986; Krokhin, Jeavons, and Jonsson 2003)....
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09 May 2011
TL;DR: A new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system and integrates several new components that are built on top of the PR2's current capabilities.
Abstract: As autonomous personal robots come of age, we expect certain applications to be executed with a high degree of repeatability and robustness. In order to explore these applications and their challenges, we need tools and strategies that allow us to develop them rapidly. Serving drinks (i.e., locating, fetching, and delivering), is one such application with well-defined environments for operation, requirements for human interfacing, and metrics for successful completion. In this paper we present our experiences and results while building an autonomous robotic assistant using the PR21 platform and ROS2. The system integrates several new components that are built on top of the PR2's current capabilities. Perception components include dynamic obstacle identification, mechanisms for identifying the refrigerator, types of drinks, and human faces. Planning components include navigation, arm motion planning with goal and path constraints, and grasping modules. One of the main contributions of this paper is a new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system. We provide in-depth discussions on the solutions that we found in accomplishing our goal, and the implementation strategies that let us achieve them.
263 citations
"Planning Domain + Execution Semanti..." refers methods in this paper
...A common way to execute and monitor a plan in ROS is by using a Finate State Machine architecture (Bohren et al. 2011)....
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