Efficient interactive decision-making framework for robotic applications
10.1016/j.artint.2015.04.004
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/108534
Tipus de documentArticle
Data publicació2017-06-01
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decision-making, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i.e., the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be hand-coded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decision-making framework for this purpose. The robot executes its plan in a step-wise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples.
Descripció
© <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
CitacióAgostini, A., Torras, C., Woergoetter, F. Efficient interactive decision-making framework for robotic applications. "Artificial intelligence", 1 Juny 2017, vol. 247, p. 187-212.
ISSN0004-3702
Versió de l'editorhttp://www.sciencedirect.com/science/article/pii/S0004370215000661
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
1648-Efficient- ... r-Robotic-Applications.pdf | 1,505Mb | Visualitza/Obre |