Dataset Recordings
RLRoverLab records datasets from examples/03_inference/eval.py when
--dataset_name is set. Files are written to
<dataset_dir>/<dataset_name>.hdf5 on environment close.
python examples/03_inference/eval.py \
--task AAURoverEnvRGBDRawWVGA-v0 \
--num_envs 1 \
--steps 1000 \
--enable_cameras \
--dataset_dir ./datasets \
--dataset_name rover_wvga_expert_1000 \
--dataset_type RL_COMPRESSED
Recorder Types
--dataset_type | Format | Use when |
|---|---|---|
RL | Legacy Isaac Lab HDF5, robomimic-style layout | A loader expects /data/demo_*/obs, /data/demo_*/next_obs, actions, rewards, and dones. |
IL | Legacy Isaac Lab HDF5, robomimic-style layout | Only observations and actions are needed. |
RL_COMPRESSED | Optimized RLRoverLab RGB-D schema | RGB-D storage size and random-access offline loading matter more than direct robomimic layout compatibility. |
Legacy HDF5
RL and IL use Isaac Lab’s default HDF5 dataset writer. The layout follows
the robomimic convention of storing demonstrations under /data/demo_N.
Current robomimic training compatibility still depends on the loader,
environment metadata, and observation keys used by the training config.
For RL, each episode contains:
| Path | Contents |
|---|---|
/data | Root data group. Attribute total is the total transition count; env_args stores environment metadata as JSON. |
/data/demo_N | One recorded episode. Attributes include num_samples and optional seed and success. |
/data/demo_N/actions | Action tensor for each transition. |
/data/demo_N/rewards | Reward tensor for each transition. |
/data/demo_N/dones | Done flags for each transition. |
/data/demo_N/obs/... | Observation at time t. |
/data/demo_N/next_obs/... | Observation after the action, at time t + 1. |
IL uses the same episode layout but records only actions and obs.
Datasets are gzip-compressed by HDF5. This format is simple and compatible with
many robomimic-style loaders, but RGB-D trajectories are large because
next_obs physically duplicates the next observation tree.
Use it with:
python examples/03_inference/eval.py \
--task AAURoverEnvRGBDRawWVGA-v0 \
--num_envs 1 \
--steps 1000 \
--enable_cameras \
--dataset_dir ./datasets \
--dataset_name rover_wvga_legacy_1000 \
--dataset_type RL
Optimized RGB-D HDF5
RL_COMPRESSED writes RLRoverLab’s optimized RGB-D HDF5 schema. It stores RGB
and depth observations once in an indexed timeline instead of duplicating a
physical next_obs tree.
Use it when RGB-D storage size and random-access offline loading matter more than direct robomimic layout compatibility. The full dataloader contract is in Optimized RGB-D HDF5.
Record an optimized RGB-D dataset with:
python examples/03_inference/eval.py \
--task AAURoverEnvRGBDRawWVGA-v0 \
--num_envs 1 \
--steps 1000 \
--enable_cameras \
--dataset_dir ./datasets \
--dataset_name rover_wvga_compressed_1000 \
--dataset_type RL_COMPRESSED