PokeFlex: A Real-World Dataset of Deformable Objects for Robotics
Keywords: Deformable objects, Robotics, 3D mesh reconstruction.
TL;DR: Novel dataset for deformable objects.
Abstract: 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 our dataset in online 3D mesh reconstruction. We refer the reader to our [website](https://anonymized-pokeflex-dataset.github.io/) or the [password protected supplementary material](https://drive.google.com/drive/folders/1d8iNoJZ0dUVlzP6XxP7xwGPhdVtwQ7du) for further demos and examples (password in pdf).
Primary Area: datasets and benchmarks
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Submission Number: 14048
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