LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hertzberg, J; Zhang, J; Zhang, L; Rockel, S; Neumann, B; Lehmann, J; Dubba, KSR; Cohn, AG; Saffiotti, A; Pecora, F; Mansouri, M; Konečný, S; Günther, M; Stock, S; Lopes, LS; Oliveira, M; Lim, GH; Kasaei, H; Mokhtari, V; Hotz, L; Bohlken, W (2014)
Publisher: Springer Verlag
Languages: English
Types: Article
Subjects:
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.

Share - Bookmark

Funded by projects

  • EC | RACE

Cite this article