# DreamTac Contact-rich Franka manipulation with vision-based tactile fingertips, as native `.mcap` episodes. A Franka Emika Panda works through contact-rich tabletop tasks while four cameras record on one 20 fps clock: a third-person view, a wrist view, and two Xense Photon vision-based tactile sensors on the gripper fingertips. Each fingertip is a gel pad printed with a marker grid that deforms where the object presses, so the moment of contact is visible rather than inferred from a force reading. 703 trajectories across 12 tasks, 506,078 frames, 7.03 hours. The tasks run from picking up a baguette or a USB plug to wiping a whiteboard and cutting a banana, and each episode also carries the end-effector pose, the gripper opening and the task instruction. **Details** - Dataset name: `dreamtac` - Dataset source: [https://huggingface.co/datasets/Voxel51/DreamTac](https://huggingface.co/datasets/Voxel51/DreamTac) - Dataset size: 2.36 GB - Dataset license: CC-BY-4.0 - Tags: `multimodal, mcap, tactile, manipulation, robotics` - Supported splits: `N/A` - ZooDataset class: [`DreamTacDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.DreamTacDataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("dreamtac") # The trajectories that moved the furthest view = dataset.sort_by("end_effector_path_m", reverse=True) session = fo.launch_app(dataset, view=view) ``` ```shell fiftyone zoo datasets load dreamtac fiftyone app launch dreamtac ```