Add model card and robotics pipeline tag

README.md

@@ -1,3 +1,54 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: robotics
+---
+
+# LoTIS: Learning to Localize Reference Trajectories in Image-Space for Visual Navigation
+
+[🤖 Demo](https://huggingface.co/spaces/fnnBsch/lotis-demo) | [📝 Paper](https://huggingface.co/papers/2602.18803) | [🏠 Project Page](https://finnbusch.com/lotis) | [💻 GitHub](https://github.com/KTH-RPL/lotis)
+
+LoTIS is a model for visual navigation that provides robot-agnostic image-space guidance by localizing a reference RGB trajectory in the robot's current view. Given a reference trajectory (a sequence of RGB images) and a query image from the robot's current viewpoint, LoTIS predicts the 2D image-space coordinates, visibility, and relative distance of each trajectory pose as it would appear in the query view.
+
+## Python API Usage
+
+To use the model programmatically, first clone the [GitHub repository](https://github.com/KTH-RPL/lotis) and follow the installation instructions to setup the environment and download the required DINOv3 backbone weights.
+
+```python
+from lotis import TrajectoryLocalizer
+
+localizer = TrajectoryLocalizer.from_checkpoint(
+    checkpoint_path="final_model.pth",
+    config_path="final_config.yaml",
+    dinov3_weights="/path/to/dinov3_vitb16_pretrain.pth",
+)
+
+# Encode a trajectory — do this once and reuse
+encoding = localizer.encode_trajectory("path/to/trajectory.mp4")
+
+# Localize a query image
+result = localizer.localize("query.jpg", encoding)
+
+print(f"Closest trajectory frame: {result.closest_frame()}")
+print(f"Visible frames: {result.visible_indices()}")
+
+# Save and reload the trajectory encoding
+import torch
+torch.save(encoding.to_dict(), "encoding.pt")
+encoding = TrajectoryEncoding.from_dict(torch.load("encoding.pt"))
+```
+
+## Citation
+
+If you find LoTIS useful in your research, please cite our work:
+
+```bibtex
+@misc{busch2026learninglocalizereferencetrajectories,
+      title={Learning to Localize Reference Trajectories in Image-Space for Visual Navigation}, 
+      author={Finn Lukas Busch and Matti Vahs and Quantao Yang and Jesús Gerardo Ortega Peimbert and Yixi Cai and Jana Tumova and Olov Andersson},
+      year={2026},
+      eprint={2602.18803},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2602.18803}, 
+}
+```