Details

Autor(en) / Beteiligte

• Shareef, Zeeshan
• Reinhart, Felix
• Steil, Jochen

Titel

Generalizing a learned inverse dynamic model of KUKA LWR IV+ for load variations using regression in the model space

Ist Teil von

• 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, p.606-611

Ort / Verlag

IEEE

Erscheinungsjahr

2016

Link zum Volltext

• Volltext

Quelle

IEL

Beschreibungen/Notizen

• In this paper, we show the generalization of an inverse dynamic model for KUKA LWR IV+ under load mass variations. We use a modular approach based on regression in the model space. First, inverse dynamic models for the known masses are learned using a recently proposed approach called Independent Joint Learning (IJL). In IJL the torque errors due to unmodeled dynamics of the real robot are estimated using only joint-local information. Second, a mapping from load mass to model parameters of torque error model is learned in order to generalize the inverse dynamics to new load masses. The modular approach improves the accuracy of an existing KUKA LWR IV+ inverse dynamic model. The results are compared with a single step IJL approach. The results show the excellent generalization for new load masses using regression in the model space.