Open AccessProceedings Article
Planning Domain + Execution Semantics: A Way Towards Robust Execution?
TLDR
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.read more
Citations
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Proceedings Article
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Journal ArticleDOI
Monitoring the execution of robot plans using semantic knowledge
TL;DR: This paper uses semantic domain knowledge to derive implicit expectations of the execution of actions in the plan, and matches these expectations against observations, and presents two realizations of this approach: a crisp one, which assumes deterministic actions and reliable sensing, and uses a standard knowledge representation system (LOOM).
Proceedings ArticleDOI
HTN robot planning in partially observable dynamic environments
TL;DR: Two approaches to dealing with incomplete world knowledge in the context of HTN robot planning are described and several experiments demonstrate that the approaches can successfully be applied in a dynamic and unstructured environment.