| 1. |
- Konečný, Štefan, 1984-, et al.
(författare)
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Execution Knowledge for Execution Monitoring : what, why, where and what for?
- 2014
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Ingår i: IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS), 2014.
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Konferensbidrag (refereegranskat)abstract
- Despite the progress made in planning androbotics, autonomous plan execution on a robot remainschallenging. One of the problems is that (classical) plannersuse abstract models which are disconnected from the sensorand actuation information available during execution. Thisconnection is typically created in a non-systematic way by somesystem-specific execution software. In this paper we proposeto explicitly represent Execution Knowledge that encodes theconnection between planning models and the actual actionsand observations for a given physical system. We present anexecution monitoring framework in which Execution Knowl-edge captures the expectations about physical plan execution.A violation of these expectations indicates an execution failure.
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| 2. |
- Konečný, Štefan, 1984-, et al.
(författare)
-
Planning domain + execution semantics : a way towards robust execution?
- 2014
-
Ingår i: Qualitative Representations for Robots. - : AAAI Press.
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Konferensbidrag (refereegranskat)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 available. 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 knowledge. 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 modularity, since the platform- and execution-specific knowledge does not need to be encoded into the planner.
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