Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction

Autor:	Saskia Golz, Sami Haddadin, Christian Osendorfer
Rok vydání:	2015
Předmět:	Engineering business.industry Feature extraction Tactile sensation Physical interaction Collision Support vector machine Human–computer interaction Sensation Robot Computer vision Artificial intelligence business Set (psychology)
Zdroj:	ICRA
DOI:	10.1109/icra.2015.7139726
Popis:	Detecting and interpreting contacts is a crucial aspect of physical Human-Robot Interaction. In order to discriminate between intended and unintended contact types, we derive a set of linear and non-linear features based on physical contact model insights and from observing real impact data that may even rely on proprioceptive sensation only. We implement a classification system with a standard non-linear Support Vector Machine and show empirically both in simulations and on a real robot the high accuracy in off- as well as on-line settings of the system. We argue that these successful results are based on our feature design derived from first principles.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e72b1829c30073c8c56a0a57aeb3460e https://doi.org/10.1109/icra.2015.7139726 Zobrazit plný text záznamu