Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations-Reference-Cited by-同舟云学术

Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations

Published:2022 Issue: Volume: Page:509-525
ISSN:2511-1256
Container-title:Springer Proceedings in Advanced Robotics
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Author:

Pacheck Adam,Konidaris George,Kress-Gazit Hadas

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95459-8_31

Reference27 articles.

1. Konidaris, G., Kaelbling, L.P., Lozano-Perez, T.: From skills to symbols: learning symbolic representations for abstract high-level planning. J. Artif. Intell. Res. 61, 215–289 (2018). https://www.jair.org/index.php/jair/article/view/11175

2. Kress-Gazit, H., Lahijanian, M., Raman, V.: Synthesis for robots: guarantees and feedback for robot behavior. Annu. Rev. Control Robot. Auton. Syst. 1, 211–236 (2018). https://www.annualreviews.org/doi/full/10.1146/annurev-control-060117-104838

3. LNCS;M Mazo,2010

4. Finucane, C., Jing, G., Kress-Gazit, H.: LTLMoP: experimenting with language, Temporal Logic and robot control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1988–1993 (2010)

5. He, K., Lahijanian, M., Kavraki, L., Moshe, V.: Automated abstraction of manipulation domains for cost-based reactive synthesis. IEEE Robot. Autom. Lett. (2018). https://ieeexplore.ieee.org/abstract/document/8585149

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