| 1. |
- Hertzberg, Joachim, et al.
(författare)
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The RACE Project : Robustness by Autonomous Competence Enhancement
- 2014
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Ingår i: Künstliche Intelligenz. - : Springer Berlin/Heidelberg. - 0933-1875 .- 1610-1987. ; 28:4, s. 297-304
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Rockel, Sebastian, et al.
(författare)
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Integrating physics-based prediction with semantic plan execution monitoring
- 2015
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Ingår i: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS). - : IEEE. - 9781479999941 ; , s. 2883-2888
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Konferensbidrag (refereegranskat)abstract
- Real-world robotic systems have to deal with uncertain and dynamic environments to reliably perform tasks. State-of-the-art cognitive robotic systems use an abstract symbolic representation of the real world that is used for high level reasoning. Some aspects of the world, such as object dynamics, are inherently difficult to capture in an abstract symbolic form, yet they influence whether the executed action will succeed or fail. This paper presents an integrated system that uses a physics-based simulation for predicting robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring. We describe a fully integrated system performing functional imagination, which is essentially contributed by a Semantic Execution Monitor (SEM). Based on information obtained from functional imagination, the robot control decides whether it is necessary to adapt the plan that is currently being executed. As a proof of concept, we demonstrate PR2 able of carrying objects on a tray without the objects toppling. Our approach achieves this by considering the robot and object dynamics in simulation. A validation shows that robot action results in simulation can be transferred to the real world. The system improves on state-of-the-art AI plan-based systems by feeding simulated prediction results back into the execution system.
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