Abstract
In the recent past, data-driven approaches have gained importance for modeling and rendering of haptic properties of deformable objects. In this paper, we propose a new data-driven approach based on a well known machine learning technique: random forest. We train the random forest for regression for estimating the input-output mapping between discrete-time interaction data (position/displacement and force) collected on a homogeneous deformable object. Unlike currently existing data-driven approaches, we use at most 1% of the recorded interaction data for the training of the random forest. Even then, the trained random forest model reproduces all the interactions used for the training with a good accuracy. This also provides promising results on unseen data. When employed for haptic rendering, the model estimates smooth and stable interaction forces at an update rate more than 650 Hz.
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2017-0-00179, HD Haptic Technology for Hyper Reality Contents).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman, L.: Machine Learning, Chap. 1, pp. 5–32. Springer (2001)
Fong, P.: Sensing, acquisition, and interactive playback of data-based models for elastic deformable objects. Int. J. Robot. Res. 28(5), 630–655 (2009)
Hover, R., Kósa, G., Szekly, G., Harders, M.: Data-driven haptic rendering–from viscous fluids to visco-elastic solids. IEEE Trans. Haptics 2(1), 15–27 (2009)
Sianov, A., Harders, M.: Data-driven haptics: addressing in homogeneities and computational formulation. In: World Haptics Conference (WHC), pp. 301–306. IEEE (2013)
Sianov, A., Harders, M.: Exploring feature-based learning for data-driven haptic rendering. IEEE Trans. Haptics 1, 1–1 (2018)
Yim, S., Jeon, S., Choi, S.: Data-driven haptic modeling and rendering of viscoelastic and frictional responses of deformable objects. IEEE Trans. Haptics 9(4), 548–559 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cha, H., Bhardwaj, A., Park, C., Choi, S. (2019). Random Forest for Modeling and Rendering of Viscoelastic Deformable Objects. In: Kajimoto, H., Lee, D., Kim, SY., Konyo, M., Kyung, KU. (eds) Haptic Interaction. AsiaHaptics 2018. Lecture Notes in Electrical Engineering, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-13-3194-7_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-3194-7_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3193-0
Online ISBN: 978-981-13-3194-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)