timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 | observation.state list | action.left_ee list | action.right_ee list | action.gripper list | action.base_vel list | action.lift_cmd list | episode_uid string | success bool | valid bool | keep bool | action.q_target list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 23 | [
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0.033333 | 1 | 0 | 1 | 23 | [
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... | [
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0.066667 | 2 | 0 | 2 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.1 | 3 | 0 | 3 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.133333 | 4 | 0 | 4 | 23 | [
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0
] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.166667 | 5 | 0 | 5 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.2 | 6 | 0 | 6 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.233333 | 7 | 0 | 7 | 23 | [
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] | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.266667 | 8 | 0 | 8 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.3 | 9 | 0 | 9 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.333333 | 10 | 0 | 10 | 23 | [
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0... | [
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0.366667 | 11 | 0 | 11 | 23 | [
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... | [
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0.4 | 12 | 0 | 12 | 23 | [
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... | [
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] | 3_cube_tower_stack_20260803_114230:000000 | true | true | true | [
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0.433333 | 13 | 0 | 13 | 23 | [
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... | [
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End of preview. Expand in Data Studio
demo_joint_space
Derived from adityx23/icl-demo-dataset
(lerobot v2.1 format, 285 episodes / 254,171 frames / 27 tasks). Every
existing column, task, episode flag (success/valid/keep), and
episode_uid is carried through unchanged.
Sibling dataset: demo_action_space adds the same episodes' Cartesian end-effector poses instead of joint targets. Same source, same episode indices, same pipeline.
What's added
One new feature, action.q_target (float32, shape [14], names
lj0..lj6, rj0..rj6): the joint-space reconstruction of each frame's
action.left_ee / action.right_ee cartesian targets, via a mink-based
IK procedure — the live teleop stack's own solver config
(FrameTask(position_cost=1.0, orientation_cost=0.1), PostureTask with
lift weighted 100x the arm joints, ConfigurationLimit, daqp, sequential
per-episode warm-starting).
Per-episode target anchoring. The recorded action.*_ee cartesian
targets carry a real, per-episode/per-arm rigid offset relative to the
reference kinematic model (confirmed empirically: near-zero in X/Y, tens of
cm in Z, stable for an entire episode/session, different between arms and
between recording sessions — consistent with a per-session tool/calibration
difference baked into the live system's own clutch anchor). Left uncorrected,
this pushes the redundant elbow/wrist joints to track a systematically
displaced target, in the worst cases hard enough to hit a mechanical joint
limit and stall convergence. Each episode's targets are anchored to that
episode's own first frame (observation.state's FK is ground truth — the
seed) before solving, removing this bias.
Orientation-glitch repair. The recorded action.{left,right}_ee
orientation occasionally snaps by 90-180 degrees for a frame or two — a
source-data artifact (observation.state stays smooth through the same
frames), not a real robot event — which would otherwise derail the
sequential warm-started solver for thousands of frames afterward. Flagged by
frame-to-frame angular velocity against the last confirmed-good frame
(handles held/duplicated glitch frames, not just single-frame spikes) and
repaired by SLERPing between the nearest good frames. A hard re-seed from
the real recorded joint state after 15 consecutive unsolved frames bounds
any stall this misses.
What's NOT included
- Videos. This repo has no
videos/directory. The source dataset's videos still apply unchanged perepisode_index— pair this repo'sdata/withadityx23/icl-demo-dataset'svideos/if you need both. - Per-frame IK convergence diagnostics (solved flag, pos/rot error) are not part of the schema — they were used to validate the reconstruction but aren't persisted here to keep the schema identical to the source plus one field. Across the full regeneration: 1,887 / 254,171 left-arm frames (0.74%) and 8,576 / 254,171 right-arm frames (3.37%) did not fully converge.
Fidelity note
The arm is 7-DOF/6-DOF-pose redundant, so action.q_target is a valid
joint configuration reaching the recorded cartesian target, not necessarily
bit-identical to the joint configuration the live system originally computed
for that same target — solving frames out of order, in parallel, or from a
different seed can converge to a different (still low pose-error) solution.
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