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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Journal ArticleDOI
Integrating multi-purpose natural language understanding, robot’s memory, and symbolic planning for task execution in humanoid robots
Mirko Wächter,Ekaterina Ovchinnikova,Valerij Wittenbeck,Peter Kaiser,Sandor Szedmak,Wail Mustafa,Dirk Kraft,Norbert Krüger,Justus Piater,Tamim Asfour +9 more
TL;DR: This study elaborate on a framework that allows a humanoid robot to understand natural language, derive symbolic representations of its sensorimotor experience, generate complex plans according to the current world state, and monitor plan execution.
Proceedings Article
Experience-based robot task learning and planning with goal inference
TL;DR: The notion of experience-based planning domains for task-level learning and planning in robotics is explored by integrating goal inference capabilities and is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.
Proceedings ArticleDOI
Integrating physics-based prediction with Semantic plan Execution Monitoring
Sebastian Rockel,Stefan Konecny,Sebastian Stock,Joachim Hertzberg,Federico Pecora,Jianwei Zhang +5 more
TL;DR: An integrated system that uses a physics-based simulation to predict robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring and improves on state-of-the-art AI plan-based systems by feeding simulated prediction results back into the execution system.
Journal ArticleDOI
Experience-Based Planning Domains: an Integrated Learning and Deliberation Approach for Intelligent Robots
TL;DR: The notion of experience-based planning domains (EBPDs) for task level learning and planning in robotics are presented and the proposed approach is illustrated and evaluated in a restaurant environment where a service robot learns how to carry out complex tasks.
Proceedings Article
Constraint-Based Online Transformation of Abstract Plans into Executable Robot Actions.
TL;DR: This paper proposes to model the internals of a robot system and its ties to the actions that the robot can perform, and proposes an online transformation of an abstract plan into executable actions conforming with system specifics.
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