Add task category, link to paper

This PR adds the `robotics` task category to the dataset card, so it can be found at https://huggingface.co/datasets?task_categories=task_categories:robotics.
It also links the paper to the Hugging Face papers page, so it can be found at https://huggingface.co/papers/2412.09617.

README.md

@@ -1,7 +1,10 @@
 ---
 license: mit
 viewer: false
+task_categories:
+- robotics
 ---
+
 # TactileTracking: A tactile-based object tracking dataset
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
@@ -11,8 +14,7 @@ viewer: false
     </video>
 </div>
 
-We present a benchmark dataset for tactile-based object tracking, featuring 12 distinct objects and 84 tracking trials—7 trials per object, each lasting an average of 10.2 seconds. The dataset includes tactile video, per-frame 6DoF ground truth sensor poses, and pre-processed surface geometry constructed from each tactile video frame. For a robust, real-time, and accurate tactile-based object tracking solution, explore our work [NormalFlow](https://github.com/rpl-cmu/normalflow). To compare NormalFlow with other methods on this dataset,use the [NormalFlow Experiments](https://github.com/rpl-cmu/normalflow_experiment) repository.
-
+We present a benchmark dataset for tactile-based object tracking, featuring 12 distinct objects and 84 tracking trials—7 trials per object, each lasting an average of 10.2 seconds. The dataset includes tactile video, per-frame 6DoF ground truth sensor poses, and pre-processed surface geometry constructed from each tactile video frame. For a robust, real-time, and accurate tactile-based object tracking solution, explore our work [NormalFlow](https://huggingface.co/papers/2412.09617). To compare NormalFlow with other methods on this dataset,use the [NormalFlow Experiments](https://github.com/rpl-cmu/normalflow_experiment) repository.
 
 ## Collection Setup
 
@@ -41,7 +43,6 @@ Each data collection trial directory contains the following components:
 - **contact_masks.npy**: An (N, H, W) array of the computed contact masks for each frame in `gelsight.mp4`, derived solely from the tactile images.
 - **gradient_maps.npy**: An (N, H, W, 2) array of the computed gradient maps for each frame in `gelsight.mp4`, based only on the tactile images.
 
-
 ## Dataset Statistics
 
 Our benchmark dataset focuses on frame-to-frame object pose tracking, with each trial ensuring overlap between the first (reference) frame and subsequent (target) frames. This setup restricts the object to local movement without long-distance shifts. The table below details the average 6DoF movement range for each object. This dataset prioritizes rotational movement, as excessive translational sliding risks damaging the sensor’s gel.