Learning Inverse Dynamics Models with Contacts

File Description SizeFormat 
icra2015_final.pdfAccepted version1.63 MBAdobe PDFDownload
Title: Learning Inverse Dynamics Models with Contacts
Author(s): Calandra, R
Ivaldi, S
Deisenroth, MP
Rueckert, E
Peters, J
Item Type: Conference Paper
Abstract: © 2015 IEEE.In whole-body control, joint torques and external forces need to be estimated accurately. In principle, this can be done through pervasive joint-torque sensing and accurate system identification. However, these sensors are expensive and may not be integrated in all links. Moreover, the exact position of the contact must be known for a precise estimation. If contacts occur on the whole body, tactile sensors can estimate the contact location, but this requires a kinematic spatial calibration, which is prone to errors. Accumulating errors may have dramatic effects on the system identification. As an alternative to classical model-based approaches we propose a data-driven mixture-of-experts learning approach using Gaussian processes. This model predicts joint torques directly from raw data of tactile and force/torque sensors. We compare our approach to an analytic model-based approach on real world data recorded from the humanoid iCub. We show that the learned model accurately predicts the joint torques resulting from contact forces, is robust to changes in the environment and outperforms existing dynamic models that use of force/ torque sensor data.
Publication Date: 1-Jan-2015
Date of Acceptance: 30-Jan-2015
URI: http://hdl.handle.net/10044/1/21852
Journal / Book Title: Proceedings of the IEEE International Conference on Robotics and Automation
Copyright Statement: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Conference Name: 2015 IEEE International Conference on Robotics and Automation (ICRA)
Start Date: 2015-05-25
Finish Date: 2015-05-30
Conference Place: Seattle, WA
Appears in Collections:Computing



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons