| --- |
| pretty_name: SemanticVLA TraceX 240K BC-Z |
| tags: |
| - robotics |
| - lerobot |
| - bc-z |
| - google-robot |
| - semanticvla |
| - tracex |
| - lerobot-v3 |
| license: other |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/**/*.parquet |
| --- |
| |
| # SemanticVLA TraceX 240K · BC-Z |
|
|
| > 🎉 **Accepted to [CVPR 2026](https://cvpr.thecvf.com/virtual/2026/poster/39352).**<br> |
| > ✍️ Fei Ni¹, Zhuo Chen², Yifu Yuan³, Zibin Dong³, Xianze Yao³, Shan Luo², Jianye Hao³, Jiankang Deng¹†, Stefanos Zafeiriou¹†<br> |
| > 🏫 ¹Imperial College London ²King's College London ³Tianjin University<br> |
| > ✉️ Primary contact: [f.ni@imperial.ac.uk](mailto:f.ni@imperial.ac.uk) |
|
|
| The **BC-Z** component of [**TraceX-240K**](https://hf.co/collections/spikefly/semanticvla-datasets) — the trace-annotated trajectory corpus introduced in **SemanticVLA**. This package is a LeRobot v3.0 repack of BC-Z · Open-X-Embodiment BC-Z v0.1.0 with **dense per-frame end-effector trace labels** stored directly in every frame row. |
|
|
| > 📚 **Code · checkpoints · paper:** https://github.com/Fei-Ni/SemanticVLA_Offcial |
| |
| ## 🎬 Sample trace overlays |
| |
| Each clip shows the orange trace label (per-frame gripper position) overlaid on the original episode camera feed. |
| |
| <table> |
| <tr> |
| <td><img src="assets/trace_overlays/ep_004331.gif" width="200" alt="episode 4331"></td> |
| <td><img src="assets/trace_overlays/ep_012997.gif" width="200" alt="episode 12997"></td> |
| <td><img src="assets/trace_overlays/ep_025940.gif" width="200" alt="episode 25940"></td> |
| <td><img src="assets/trace_overlays/ep_030275.gif" width="200" alt="episode 30275"></td> |
| </tr> |
| <tr> |
| <td align="center"><sub>episode 4331</sub></td><td align="center"><sub>episode 12997</sub></td><td align="center"><sub>episode 25940</sub></td><td align="center"><sub>episode 30275</sub></td> |
| </tr> |
| </table> |
| |
| ## 🧭 Trace label schema |
| |
| Dense per-frame end-effector position columns, stored directly in every frame row: |
| |
| - `trace.x` — `float32[1]`, normalized image-space x coordinate on a `[0, 100]` scale. |
| - `trace.y` — `float32[1]`, normalized image-space y coordinate on a `[0, 100]` scale. |
| |
| `(0, 0)` is the top-left corner of the frame, `(100, 100)` the bottom-right. |
| |
| ## 📦 Contents |
| |
| | Field | Value | |
| |---|---| |
| | HF repo | `spikefly/SemanticVLA-TraceX-240K-BC-Z` | |
| | Format | LeRobot v3.0 | |
| | Episodes | 43,264 | |
| | Frames | 6,015,535 | |
| | FPS | 5 | |
| | Robot type | `google_robot` | |
| | Source dataset | Open-X-Embodiment BC-Z v0.1.0 | |
| | Data file target | 100 MB | |
| | Video file target | 500 MB | |
|
|
| ### Video streams |
|
|
| - `observation.images.image` |
|
|
| ## 🧭 Coverage |
|
|
| Episodes are stored in a single continuous index space: 39,350 train + 3,914 val = 43,264 total. Source split is preserved in episode metadata. |
|
|
| ## 📂 Sibling datasets in TraceX-240K |
|
|
| <table> |
| <tr><th>Dataset</th><th>HF Repository</th><th>Embodiment</th></tr> |
| <tr><td><b>Bridge</b></td><td><a href="https://huggingface.co/datasets/spikefly/SemanticVLA-TraceX-240K-Bridge">spikefly/SemanticVLA-TraceX-240K-Bridge</a></td><td>BridgeData V2 / WidowX</td></tr> |
| <tr><td><b>Fractal</b></td><td><a href="https://huggingface.co/datasets/spikefly/SemanticVLA-TraceX-240K-Fractal">spikefly/SemanticVLA-TraceX-240K-Fractal</a></td><td>Fractal / Google Robot (RT-1)</td></tr> |
| <tr><td><b>DROID</b></td><td><a href="https://huggingface.co/datasets/spikefly/SemanticVLA-TraceX-240K-DROID">spikefly/SemanticVLA-TraceX-240K-DROID</a></td><td>DROID / Franka</td></tr> |
| </table> |
|
|
| ## ✏️ Citation |
|
|
| If you use this dataset, please cite SemanticVLA: |
|
|
| ```bibtex |
| @inproceedings{ni2026semanticvla, |
| title = {SemanticVLA: Towards Semantic Reasoning over Action Memorization via Synergistic Explicit Trace and Latent Action Planning}, |
| author = {Ni, Fei and Chen, Zhuo and Yuan, Yifu and Dong, Zibin and Yao, Xianze and Luo, Shan and Hao, Jianye and Deng, Jiankang and Zafeiriou, Stefanos}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## 📜 License & terms |
|
|
| The trace annotation labels (`trace.x` / `trace.y`) are released under the [MIT License](https://github.com/Fei-Ni/SemanticVLA_Offcial/blob/main/LICENSE) as part of the SemanticVLA code release. Use of the underlying robot trajectory data is subject to the **original source dataset terms** (Open-X-Embodiment BC-Z v0.1.0). Please consult the upstream dataset license before use. |
|
|
