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PokeFlex is a dataset containing real-world paired and annotated multimodal data of deformable objects
that includes 3D textured meshes, point clouds, RGB images, and depth maps.
Data-driven methods have shown great potential in solving challenging manipulation tasks, however, their application in the domain of deformable objects has been constrained, in part, by the lack of data. To address this, we propose PokeFlex, a dataset featuring real-world paired and annotated multimodal data that includes 3D textured meshes, point clouds, RGB images, and depth maps. Such data can be leveraged for several downstream tasks such as online 3D mesh reconstruction and it can potentially enable underexplored applications such as the real-world deployment of traditional control methods based on mesh simulations. To deal with the challenges posed by real-world 3D mesh reconstruction, we leverage a professional volumetric capture system that allows complete 360° reconstruction. PokeFlex consists of 18 deformable objects with varying stiffness and shapes. Deformations are generated by dropping objects onto a flat surface or by poking the objects with a robot arm. Interaction forces and torques are also reported for the latter case. Using different data modalities, we demonstrated a use case for the PokeFlex dataset in online 3D mesh reconstruction.
Top: Mesh reconstructions of foam dice for a poking sequence shown in every third frame.
Bottom: Mesh reconstructions of plush octopus for a dropping sequence.
Superimposed representation of the proposed network architectures for ingesting the multi-modal PokeFlex data to predict deformed mesh reconstruction. Inference rates across different data modalities range from 106 Hz to 215 Hz for dense point clouds and forces, respectively. (See paper for more details).
@article{obrist2024pokeflex,
author = {Obrist, Jan and Zamora, Miguel and Zheng, Hehui and Hinchet, Ronan and Ozdemir, Firat and Zarate, Juan and Katzschmann, Robert K. and Coros, Stelian},
title = {PokeFlex: A Real-World Dataset of Deformable Objects for Robotics},
journal = {Under review},
year = {2024}
url = {https://arxiv.org/pdf/2410.07688}
}