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.
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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.
26 citations
References
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Proceedings Article•
01 Aug 2005
TL;DR: This paper proposes and gives experimental results of an original approach for temporal planning and execution control, including plan repair and replanning, fully integrated onboard a robot performing rover exploration like missions, which can be integrated in existing architectures and used onboard a fully operational robot, with currently available hardware.
Abstract: To achieve the ever increasing demand for science returns, extraterrestrial exploration rovers require more autonomy to successfully perform their missions. Indeed, the communication delays are such that teleoperation is unrealistic. Although the current rovers (such as MER) demonstrate a limited navigation autonomy, and mostly rely on ground mission planning, the next generation (e.g. NASA Mars Science Laboratory and ESA Exomars) aims at “beyond the field of view” autonomous navigation. Other exploration missions which cannot rely on human teleprogramming, will even require activity planning, repair and replanning to be made onboard. In this paper, we propose and give experimental results of an original approach for temporal planning and execution control, including plan repair and replanning, fully integrated onboard a robot performing rover exploration like missions. Our claim is twofold. First these planning/plan repair methods and techniques are now mature enough to be considered to solve real world problems. Second they can be integrated in existing architectures and used onboard a fully operational robot, with currently available hardware.
7 citations
"Planning Domain + Execution Semanti..." refers background in this paper
...In temporal planning and scheduling (Gallien, Ingrand, and Lemai 2004; Fratini, Pecora, and Cesta 2008; Barreiro et al. 2012; Di Rocco, Pecora, and Saffiotti 2013) temporal and resource knowledge about execution is used to detect failure....
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