| --- |
| license: apache-2.0 |
| pretty_name: Orbit-Planner Orbital Evasion Dataset |
| task_categories: |
| - robotics |
| tags: |
| - '- spacecraft' |
| - '- obstacle-avoidance' |
| - '- world-models' |
| - '- isaac-sim' |
| - '- trajectory-prediction' |
| --- |
| |
| # Orbit-Planner Orbital Evasion Dataset |
|
|
|
|
| <p align="left"> |
| <a href="https://arxiv.org/abs/2608.16651"><img src="https://img.shields.io/badge/arXiv-Paper-B31B1B?logo=arxiv&logoColor=white" alt="ArXiv Paper"></a> |
| <a href="https://github.com/ZhijianLi2003/Orbit_Planner"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white" alt="GitHub Code"></a> |
| <a href="https://zhijianli2003.github.io/Orbit_Planner/"><img src="https://img.shields.io/badge/Project-Page-0A7EA4?logo=googlechrome&logoColor=white" alt="Project Page"></a> |
| </p> |
|
|
| Orbit-Planner is a simulated multimodal trajectory dataset for vision-based |
| spacecraft navigation and obstacle avoidance. It contains synchronized |
| first-person RGB images, depth maps, spacecraft states, thruster commands, and |
| event labels collected in the Orbital Evasion task from Space Robotics Bench |
| and NVIDIA Isaac Sim. |
|
|
| The dataset is intended for learning latent world models, spacecraft dynamics, |
| visual representations, imitation policies, and collision-aware planning. Its |
| trajectories cover successful obstacle avoidance, direct high-risk flight, and |
| open-loop exploration, providing both task-oriented behavior and diverse |
| spacecraft dynamics. |
|
|
| # Dataset at a Glance |
|
|
| | Property | Value | |
| | --- | ---: | |
| | Number of trajectories | 8,000 | |
| | Total time steps | 2,114,895 | |
| | Time between consecutive frames | 0.04 s (25 Hz) | |
| | Average steps per trajectory | 264.36 | |
| | Trajectories with success events | 3,437 | |
| | Trajectories with collision events | 1,530 | |
| | State dimension | 16 | |
| | Action dimension | 8 | |
| | Event dimension | 2 | |
| | RGB resolution | 224 x 224 x 3 | |
| | Depth resolution | 224 x 224 | |
|
|
| The dataset contains the following collection strategies: |
|
|
| | Strategy | Trajectories | Description | |
| | --- | ---: | --- | |
| | `expert` | 5,601 | RRT* obstacle-avoiding path planning followed by path smoothing and PD tracking. | |
| | `risky` | 1,578 | Direct flight toward the target without obstacle-avoiding path planning. | |
| | `explore` | 821 | Segmented near-open-loop force, torque, combined-control, and coasting behavior. | |
|
|
|
|
| # Data Structure |
|
|
| The HDF5 data are organized as one group per trajectory: |
|
|
| ```text |
| dataset.h5 |
| +-- traj_0000/ |
| | +-- states [T, 16] float64 |
| | +-- actions [T, 8] float32 |
| | +-- events [T, 2] float32 |
| | +-- rgb [T, 224, 224, 3] uint8 |
| | +-- depth [T, 224, 224] uint8 |
| +-- traj_0001/ |
| | +-- ... |
| +-- ... |
| ``` |
|
|
| All arrays are gzip-compressed in the HDF5 file. The accompanying |
| [`meta.json`](./meta.json) contains the full dataset configuration, feature |
| definitions, aggregate statistics, and a trajectory-level index. |
|
|
| ## State |
|
|
| The state vector is |
|
|
| ```text |
| s_t = [p_t, v_t, rot6d_t, omega_t, phi_t] |
| ``` |
|
|
| | Slice | Size | Description | |
| | --- | ---: | --- | |
| | `p_t` (`0:3`) | 3 | Spacecraft displacement from its initial position in the world frame. | |
| | `v_t` (`3:6`) | 3 | Linear velocity in the body frame. | |
| | `rot6d_t` (`6:12`) | 6 | First two columns of the rotation matrix. | |
| | `omega_t` (`12:15`) | 3 | Angular velocity in the body frame. | |
| | `phi_t` (`15:16`) | 1 | Remaining fuel fraction in `[0, 1]`. | |
|
|
| ## Action |
|
|
| The action vector contains normalized commands for eight spacecraft thrusters: |
|
|
| ```text |
| a_t = [F1, F2, ..., F8], a_t in [0, 1]^8 |
| ``` |
|
|
| ## Events |
|
|
| The event vector is |
|
|
| ```text |
| e_t = [collision, success] |
| ``` |
|
|
| `collision` is the event label intended for downstream use and marks steps |
| where the velocity-based impulse proxy exceeds its configured threshold. |
| `success` is retained only as a collection-time bookkeeping field and is not |
| used by the current training or evaluation pipeline, so it can be ignored. |
| Event values are binary and stored as `float32`. |
|
|
| ## Visual Observations |
|
|
| Each step includes a first-person `uint8` RGB frame and a grayscale `uint8` |
| depth frame. Depth values use an inverted encoding in which larger values are |
| nearer. Convert a stored depth value `d` to meters with: |
|
|
| ```text |
| depth_m = ((255 - d) / 255.0) * 50.0 + 0.1 |
| ``` |
|
|
| The valid clipping range is 0.1 m to 50.1 m, and background space is encoded as |
| zero. |
|
|
| ## Trajectory Metadata |
|
|
| Each trajectory has an identifier and summary attributes including |
| `total_steps`, `has_collision`, `has_success`, `terminated`, `truncated`, |
| `target_pos`, `strategy`, and `collision_step`. A `collision_step` value of |
| `-1` means that no collision was detected; otherwise, it identifies the first |
| collision frame. |
|
|
| # Intended Uses |
|
|
| This dataset is suitable for: |
|
|
| - multimodal and latent spacecraft world models; |
| - visual dynamics and future-state prediction; |
| - imitation learning and offline reinforcement learning; |
| - obstacle-avoidance planning and control; |
| - collision prediction and post-impact dynamics modeling; |
| - benchmarking state, depth, and action representation learning. |
|
|
| Trajectory-level splits should be used to prevent adjacent frames from the same |
| episode from appearing in both training and evaluation sets. |
|
|
| # Limitations |
|
|
| The data are generated entirely in simulation and do not capture every source |
| of real spacecraft sensor noise, actuator uncertainty, illumination variation, |
| or contact dynamics. Behavior comes from scripted expert, risky, and exploration |
| controllers rather than human demonstrations. Collision labels are based on a |
| velocity-change threshold, so they should not be treated as direct contact |
| sensor measurements. The strategy and outcome distributions are also |
| imbalanced and should be considered when constructing evaluation splits. |
|
|
| # Citation |
|
|
| If you use this dataset in your research, please cite our |
| [Paper](https://arxiv.org/abs/2608.16651): |
|
|
| ```bibtex |
| @article{li2026orbitplanner, |
| title = {Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agent}, |
| author = {Li, Zhijian and Ren, Chao and Wang, Peijin and Sun, Xian}, |
| journal = {arXiv preprint arXiv:2608.16651}, |
| year = {2026} |
| } |
| ``` |
