Learning from Humans—Computational Models of Cognition-Enabled Control of Everyday Activity
| dc.contributor.author | Beetz, Michael | |
| dc.contributor.author | Buss, Martin | |
| dc.contributor.author | Radig, Bernd | |
| dc.date.accessioned | 2018-01-08T09:14:41Z | |
| dc.date.available | 2018-01-08T09:14:41Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | In recent years, we have seen tremendous advances in the mechatronic, sensing and computational infrastructure of robots, enabling them to act in several application domains faster, stronger and more accurately than humans do. Yet, when it comes to accomplishing manipulation tasks in everyday settings, robots often do not even reach the sophistication and performance of young children. In this article, we describe an interdisciplinary research approach in which we design computational models for controlling robots performing everyday manipulation tasks inspired by the observation of human activities. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11167 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 24, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Learning from Humans—Computational Models of Cognition-Enabled Control of Everyday Activity | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 318 | |
| gi.citation.startPage | 311 |