license: apache-2.0
task_categories:
- robotics
tags:
- robotics
- retargeting
- agilex-piper
- lerobot
piper-retargeted
Human demonstrations retargeted to an AgileX Piper arm with the Fidelity Dynamics dt-pipeline (stage 6). Two datasets, one repo.
| archive | size | clips | frames | source |
|---|---|---|---|---|
piper_ego_retargeted.tar |
2.2 GB | 1,574 | 164,601 | EgoDex-derived egocentric pick-and-place (ego_filtered, LeRobot v3.0, 30 tasks) |
piper_stera_plate_retargeted.tar |
332 MB | 226 | 30,584 | stera-10m plate handling (plate200_pp) |
Each archive is self-contained: the observation video sits in the same clip directory as the trajectory it belongs to, so there is nothing to join.
Layout
<dataset>/clips/<clip_id>/observation.mp4 # RGB observation for this clip
<dataset>/clips/<clip_id>/retargeted.hdf5 # trajectories + per-frame IK error
<dataset>/clips/<clip_id>/report.json # per-clip M1–M6 record, QA grades
<dataset>/manifest.json # aggregate stats + one row per clip
<dataset>/README.md
Video and trajectory are 1:1 per clip and share the clip's frame count and 30 fps
timebase. Archives are uncompressed .tar — the MP4s are already compressed, so
gzip would cost time and save nothing.
retargeted.hdf5:
| key | shape | meaning |
|---|---|---|
joint_positions |
(T, 14) | arms in R, L order — 6 joints + 1 gripper each |
ik_errors_R / _L |
(T,) | per-frame IK position error, metres |
base_R / _L |
(3,) | base placement chosen by BPP |
timestamps |
(T,) | seconds, 30 fps |
Attributes carry joint_names, active_arms, parked_arms, arm_mode,
n_arms, fps and the M4/QA records.
Robot
agilex_piper_bimanual. Stage 6 loads one single-arm MJCF and drives it once
per arm with its own base placement, so "bimanual" needs no two-arm model.
Joints joint1..joint6 plus jaw joint7 (joint8 is coupled = -joint7).
The TCP is the grasp_frame fingertip midpoint, not Link6.
Workspace placement used WORKSPACE_CENTER_MODE=capmap: the centre is the
reachability-index-weighted centroid of this arm's own pose-reachable voxels
([0.034, 0.000, 0.208] over 18,479 voxels), not a reference robot's vector
rescaled. Scale 0.8811 (reach ratio 1.037; Piper 0.897 m vs reference 0.865 m).
Quality
| ego | stera | |
|---|---|---|
| FK error (worst active arm) | mean 4.48 cm, median 3.87, p90 7.63 | mean 10.53 cm, median 7.20, p90 24.66 |
| saturation | mean 0.426 | mean 0.506 |
| QA PASS / WARN / FAIL | 262 / 1012 / 300 | 19 / 111 / 96 |
| dominant FAIL cause | saturation (331) | saturation (89), self-collision (18) |
Filter, don't discard. qa_verdict gates on the worst single arm joint's
fraction of frames pinned at a limit, so one saturated DOF fails a clip whose
tracking is otherwise good. manifest.json carries per-clip ik_mean_cm and
saturation if you would rather threshold yourself. The stera set is
substantially the weaker of the two.
Per-dataset notes
ego — both arms are active on every clip, but not equally busy: the right arm travels a median 1.00 m per clip against the left's 0.025 m and carries the error (4.25 vs 0.44 cm). A near-static arm can still saturate by resting against a limit; 139 of the 300 FAILs are left-arm.
stera — single-arm (--arm right); the left arm is a constant park pose
carrying no signal, so drop columns 7–13 for single-arm training. 29 of the
255 selected clips are absent because the human demonstrator used their left
hand, dropping the right below the 50% tracking threshold; they are listed in
manifest.json under skipped_clips. Because M3.5 re-solves the moving arm to
avoid the parked one, 18 clips FAIL on selfcol for a collision that will not
exist once the left arm is dropped.
Known limit
About 73% of Piper's joint saturation is wrist-driven (joint4 and joint5; joint5's range is only ±1.22 rad, the tightest on the arm). A workspace re-placement cannot fix an orientation the wrist cannot reach, which is why the teleop-supervised refit is disabled for these runs — measured, it accepted 2 of 150 clips. The remaining lever is orientation-side, not placement.