TY  - BOOK
AB  - Task learning from observations of non-expert human users will be a core feature of future cognitive robots. However, the problem of task segmentation has only received minor attention. In this paper, we present a new approach to classifying and segmenting series of observations into a set of candidate motions. As basis for these candidates, we use structured UKR manifolds, a modified version of unsupervised kernel regression which has been introduced in order to easily reproduce and synthesise represented dextrous manipulation tasks. Together with the presented mechanism, it then realises a system that is able both to reproduce and recognise the represented motions.
DA  - 2009
DO  - 10.1109/IROS.2009.5354030
LA  - eng
PY  - 2009
TI  - Using Structured UKR Manifolds for Motion Classification and Segmentation
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-19969025
Y2  - 2024-11-22T01:14:42
ER  -