PokeFlex: A Real-World Dataset of Deformable Objects for Robotics.

Jan Obrist, Miguel Angel Zamora Mora, Hehui Zheng, Ronan Hinchet, Firat Ozdemir, Juan Jose Zarate, Robert K. Katzschmann, Stelian Coros

Keywords: Deformable objects, Robotics, multimodal data, 3D mesh reconstruction.

TL;DR: Novel dataset for deformable objects.

Abstract: This paper proposes PokeFlex, a dataset featuring real-world paired and annotated multimodal data that includes 3D textured meshes, point clouds, RGB images, and depth maps. 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. We refer the reader to our [website](https://pokeflex-dataset.github.io/) for demos and examples of our dataset.

Submission Number: 33