dxl-rosbag-postprocess

name

dxl-rosbag-postprocess

description

After a rosbag2 collection session in dxl-teleop-bilateral, copy the recorded task directory from local_dataset/ into the canonical rosbags/ tree, run the rosbag2->LeRobot conversion via temporal_force_encoding's pixi task, and validate the resulting dataset (action present, videos present, frame counts consistent). Use this whenever the user has just finished collecting episodes and wants the LeRobot dataset built and sanity-checked.

DXL rosbag → LeRobot post-processing

After a dxl_data_collection session, raw rosbags land under dxl-teleop-bilateral/dxl_arm_ros/dxl_data_collection/local_dataset/<task>-<robot>-<location>/. The downstream training pipeline expects them at /home/tatsuya.kamijo/dataset/bilateral_imitation/rosbags/<task>-<robot>-<location>/ and the converted LeRobot output at /home/tatsuya.kamijo/dataset/bilateral_imitation/lerobot/<dataset_name>/.

This skill takes a freshly recorded task directory through three deterministic steps: copy → convert → validate.

Inputs

Required from the user (or inferred):

• <task_dir_name> — e.g. pose_teleop_test-crane-x7-kadokawa. The directory under local_dataset/. The trailing -<robot>-<location> suffix is part of the convention created by rosbag_manager.py.
• <dataset_name> — short identifier for the LeRobot output dataset folder. Default to <task_name without suffix> (e.g. pose_teleop_test).
• <conversion_config> — relative path under temporal_force_encoding/config/conversion/. Default: cube_position_v3.yaml (the latest single-arm 24D state / 8D action config with state_as_action: false, action read from /follower/arm_trajectory_controller/command). Other usable configs: single_position.yaml (now also state_as_action: false).
• <target_freq> — Hz to resample to. Default: 50. The pose_teleop launches publish at 50 Hz; conversion at 50 Hz keeps source resolution.

If the user just says "post-process", ask only for the task dir name and default the rest. Don't pause for items where a sensible default exists.

Step 1 — copy from local_dataset to dataset/rosbags

Pre-flight: confirm the source exists. If it does not, stop here and ask the user to confirm the task name. Do NOT pick a near-name sibling automatically — the -<robot>-<location> suffix and prefix are load-bearing (the LeRobot output dir and downstream pixi tasks default off the prefix).

SRC=/home/tatsuya.kamijo/dxl-teleop-bilateral/dxl_arm_ros/dxl_data_collection/local_dataset/<task_dir_name>
DST=/home/tatsuya.kamijo/dataset/bilateral_imitation/rosbags/<task_dir_name>

if [ ! -d "$SRC" ]; then
    echo "Source not found: $SRC"
    echo "Available task dirs:"
    ls /home/tatsuya.kamijo/dxl-teleop-bilateral/dxl_arm_ros/dxl_data_collection/local_dataset/
    exit 1
fi

mkdir -p "$DST"
cp -rv "$SRC/." "$DST/"
ls -la "$DST"

Notes:

• cp -r "$SRC/." "$DST/" copies contents into DST without nesting; this matters when DST already has a meta.json from previous sessions.
• After the copy, files are owned by the host user (the rosbag_manager umask 0o000 change and the post-recording chmod -R a+rwX make this work even for files that were originally written by container root).
• Do not delete the source. Subsequent collection sessions append more episodes there; we sync, not move.

Step 2 — convert rosbag → LeRobot

cd /home/tatsuya.kamijo/temporal_force_encoding
pixi run python scripts/rosbag2lerobot.py \
    /home/tatsuya.kamijo/dataset/bilateral_imitation/rosbags/<task_dir_name> \
    --config config/conversion/<conversion_config> \
    --output-dir /home/tatsuya.kamijo/dataset/bilateral_imitation/lerobot/<dataset_name> \
    --target-freq <target_freq>

Pre-flight: LeRobotDataset.create does mkdir(exist_ok=False) and will crash with FileExistsError if the output directory already exists.

OUT=/home/tatsuya.kamijo/dataset/bilateral_imitation/lerobot/<dataset_name>
if [ -d "$OUT" ]; then
    mv "$OUT" "${OUT}.bak_$(date +%Y%m%d_%H%M%S)"
fi

NEVER rm -rf the output dir. The user has a hard rule on never deleting model/dataset output dirs — always rename to a .bak_* sibling and let them clean up later. (See ~/.claude/projects/.../memory/feedback_never_delete_checkpoints.md.)

Step 3 — validate the LeRobot dataset

cd /home/tatsuya.kamijo/temporal_force_encoding
pixi run python scripts/validate_lerobot_dataset.py \
    --dataset /home/tatsuya.kamijo/dataset/bilateral_imitation/lerobot/<dataset_name>

Expected success line:

[validate] OK: <N> episodes, <F> frames, 2 video stream(s) per episode all present.

The validator checks (read it for the full list):

• per-episode parquet file exists
• per-episode video files exist for every dtype: video feature
• action column is not all-zero (catches the case where the chosen action topic was empty — e.g. picking /leader/... for a pose_teleop bag)
• observation.state is finite, not all-zero
• frame counts match info.json

If validation fails:

• "action all-zero" → wrong action topic in the conversion config (for pose_teleop bags, action lives on /follower/arm_trajectory_controller/command, not /leader/...).
• "video missing" → check the rosbag actually has the camera topic; pose_teleop launches both usbcam_top and usbcam_right — if either device was missing, the bag has no frames for that topic and the converter would have no images to encode.
• frame mismatch → don't paper over; surface to the user.

After all three steps

Report a single short summary to the user:

copied:  <task_dir_name> -> rosbags/
converted: <N> episodes, <F> frames @ <target_freq> Hz
validated: OK
output: /home/tatsuya.kamijo/dataset/bilateral_imitation/lerobot/<dataset_name>

Do NOT proactively kick off training, replay, or further conversion variants (e.g. trim_state_to_pos_vel). The user drives the next step.

Conversion config quick reference

Config	repo_id	state	action	state_as_action
cube_position_v3.yaml	single_cube_v3	16D (pos+vel × 8, no effort)	8D follower pos	false (preferred for pose_teleop / bilateral collected after 2026-05; matches policy input layout directly — no trim_state_to_pos_vel follow-up needed)
cube_position.yaml	single_cube	24D	8D	true (legacy)
single_position.yaml	single_position_control	24D	8D	false (post 2026-05-05 edit; was true before)

When in doubt and the data was collected with the current pose_teleop.launch.py (which now publishes /leader_follower_joint_states), use cube_position_v3.yaml.

State layout knob

rosbag2lerobot.py reads topics.state_layout from the config:

• pos_vel_eff (default if absent): 24D — [pos, vel, eff] × 8 per joint.
• pos_vel: 16D — [pos, vel] × 8 per joint, effort dropped. The script auto-overrides features.observation.state.shape and names to match, so the user only flips the one knob. cube_position_v3.yaml already sets this; legacy configs default to pos_vel_eff for backward compat.

When state_layout: pos_vel is in effect, trim_state_to_pos_vel.py is no longer needed as a downstream pass — the _pos16 / _16 dataset is what the converter produces directly.

Pre-existing failure modes worth checking up-front

• Source local_dataset/<task_dir_name> not found → halt at Step 1, list the candidate task dirs, and ask the user. Never auto-pick a near-name match.
• Output directory already exists → backup, don't delete.
• local_dataset/ files owned by root from a pre-fix container run → workaround: chmod -R a+rwX from inside the container, not sudo from host (avoid escalating).
• pose_teleop bag has empty /leader/arm_trajectory_controller/command connection (pose_teleop never publishes leader effort). The validator's "action all-zero" check catches this when the conversion config wrongly selected the leader topic.