SuDA: Support-based domain adaptation for Sim2Real hinge joint tracking with flexible sensors

Fang, Jiawei, Song, Haishan, Zuo, Chengxu, Gao, Xiaoxia, Chen, Xiaowei, Guo, Shihui and Qin, Yipeng ORCID: https://orcid.org/0000-0002-1551-9126 2024. SuDA: Support-based domain adaptation for Sim2Real hinge joint tracking with flexible sensors. Presented at: The Forty-First International Conference on Machine Learning (ICML), Vienna, Austria, 21 - 27 July 2024. Published in: Salakhutdinov, R., Kolter, Z., Heller, K., Weller, A., Oliver, N., Scarlett, J. and Berkenkamp, F. eds. Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research. , vol.235 ML Research Press, pp. 22042-22061.

Preview

PDF - Accepted Post-Print Version Download (6MB) | Preview

Abstract

Flexible sensors hold promise for human motion capture (MoCap), offering advantages such as wearability, privacy preservation, and minimal constraints on natural movement. However, existing flexible sensor-based MoCap methods rely on deep learning and necessitate large and diverse labeled datasets for training. These data typically need to be collected in MoCap studios with specialized equipment and substantial manual labor, making them difficult and expensive to obtain at scale. Thanks to the high-linearity of flexible sensors, we address this challenge by proposing a novel Sim2Real solution for hinge joint tracking based on domain adaptation, eliminating the need for labeled data yet achieving comparable accuracy to supervised learning. Our solution relies on a novel Support-based Domain Adaptation method, namely SuDA, which aligns the supports of the predictive functions rather than the instance-dependent distributions between the source and target domains. Extensive experimental results demonstrate the effectiveness of our method and its superiority overstate-of-the-art distribution-based domain adaptation methods in our task.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	ML Research Press
ISSN:	2640-3498
Date of First Compliant Deposit:	24 June 2024
Date of Acceptance:	2 May 2024
Last Modified:	19 Feb 2026 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/169373

Actions (repository staff only)

Edit Item