Contact-rich manipulation has become increasingly important in robot learning. However, previous studies on robot learning datasets have focused on rigid objects and underrepresented the diversity of pressure conditions for real-world manipulation. To address this gap, we present a humanoid visual-tactile-action dataset designed for manipulating deformable soft objects. The dataset was collected via teleoperation using a humanoid robot equipped with dexterous hands, capturing multi-modal interactions under varying pressure conditions. This work also motivates future research on models with advanced optimization strategies capable of effectively leveraging the complexity and diversity of tactile signals.
@article{arxiv.2510.25725,
title = {A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation},
author = {Eunju Kwon and Seungwon Oh and In-Chang Baek and Yucheon Park and Gyungbo Kim and JaeYoung Moon and Yunho Choi and Kyung-Joong Kim},
journal= {arXiv preprint arXiv:2510.25725},
year = {2025}
}