--- 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

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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} } ```