Tracking cloth deformation: a novel dataset for closing the sim-to-real gap for robotic cloth manipulation learning
Tracking cloth deformation: a novel dataset for closing the sim-to-real gap for robotic cloth manipulation learning
We present a cloth dataset consisting of 120 high-quality recordings of several textiles during dynamic motions. Using a Motion Capture System, we record the location of key-points on the cloth surface of four types of fabrics (cotton, denim, wool and polyester) of two sizes (A2 and A3) and at different speeds. The scenarios considered are all dynamic and involve rapid shaking and twisting of the textiles, collisions with frictional objects, strong hits with a long rigid object and even self-collisions. For more information about the dataset, see: Franco Coltraro, Júlia Borràs, Maria Alberich-Carramiñana and Carme Torras. Tracking cloth deformation: a novel dataset for closing the sim-to-real gap for robotic cloth manipulation learning to appear in International Journal of Robotics Research (2025).
- F. Coltraro is supported by Momentum CSIC Programme project MMT24-IRII-01 and by RobIRI 2021 SGR 00514. - J. Borras is supported by the project PID2020-118649RB-I00(CHLOE-GRAPH) funded by MCIN/ AEI /10.13039/501100011033.
Peer reviewed
Sim-to-real gap, Motion Capture, Cloth manipulation, sim-to-real gap, cloth manipulation, robotic learning, Robotic learning
Sim-to-real gap, Motion Capture, Cloth manipulation, sim-to-real gap, cloth manipulation, robotic learning, Robotic learning
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