# HapTile Teleoperated contact-rich manipulation with vision-based tactile fingertips and the operator’s haptic feedback, as native `.mcap` episodes. A UR5e arm with a Robotiq 2F-85 gripper works through contact-rich tabletop tasks under teleoperation. Each gripper finger carries a vision-based tactile sensor that films a gel pad printed with a marker grid, so the contact shows as the markers moving. Two RGB-D cameras watch the scene, one facing the table and one on the wrist, and the haptic feedback the operator felt is recorded alongside the robot state and the language instruction. 1,699 episodes across 38 tasks, 664,045 frames, 12.36 hours. The tasks run from inserting a peg and folding a T-shirt to pouring from a bottle and wiping a whiteboard. **Details** - Dataset name: `haptile` - Dataset source: [https://huggingface.co/datasets/Voxel51/HapTile](https://huggingface.co/datasets/Voxel51/HapTile) - Dataset size: 6.32 GB - Dataset license: CC-BY-4.0 - Tags: `multimodal, mcap, tactile, haptics, manipulation` - Supported splits: `N/A` - ZooDataset class: [`HapTileDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.HapTileDataset) **Example usage** Python CLI ```python import fiftyone as fo import fiftyone.zoo as foz dataset = foz.load_zoo_dataset("haptile") # The firmest contacts on the right fingertip view = dataset.sort_by("peak_marker_motion_right", reverse=True) session = fo.launch_app(dataset, view=view) ``` ```shell fiftyone zoo datasets load haptile fiftyone app launch haptile ```