LeeHakHo/square_real30

Square (NutAssemblySquare) — 30 real Franka demonstrations

Real-robot counterpart to the square_d2 task in the ID90 / OOD auxiliary-pose study. A Franka Panda picks a square nut by its handle and drops it onto a square peg. Every episode is a successful insertion.

Collected 2026-09-07 on the earlgrey rig. Companion to the simulated datasets in LeeHakHo/mimicgen_aux_data.

Demonstrations	30
Samples	10,741
Control / recording rate	15 Hz
Episode length	289 – 440 frames (19 – 29 s)
Size	0.28 GB
Format	robomimic HDF5

Layout

data/demo_<i>/
  obs/agentview_image            (T,84,84,3) uint8   Intel RealSense D435, static, scene
  obs/robot0_eye_in_hand_image   (T,84,84,3) uint8   ZED Mini, wrist-mounted
  obs/robot0_eef_pos             (T,3)   float64     panda_hand_tcp, robot base frame
  obs/robot0_eef_quat            (T,4)   float64     xyzw
  obs/robot0_gripper_qpos        (T,2)   float64     [+f, -f], robosuite convention
  obs/object                     (T,7)   float64     block 0 = nut [pos(3) | quat(4) xyzw]
  obs/aux_valid                  (T,)    uint8       see "Gating the auxiliary loss"
  actions                        (T,7)   float64     [dpos(3) | drot axis-angle(3) | grasp]
  rewards, dones                 (T,)                sparse: 1 on the final frame
  joint_qpos                     (T,7)   float64     arm joints, provenance
mask/train  (27 demos)   mask/valid  (3 demos)

Images were captured at 1280x720, centre-cropped to square and resized to 84x84. The crop keeps the whole workspace at this camera pose; squashing 16:9 straight to a square would have distorted every object.

Gating the auxiliary loss with obs/aux_valid

obs/aux_valid is 1 where the object pose in obs/object can be trusted and 0 where it cannot. If you train an auxiliary pose head on this dataset, multiply the per-frame auxiliary loss by it. 97.6% of frames are valid (258 of 10,741 masked out).

Why the mask exists

obs/object is not ground truth. It comes from FoundationPose tracking RGB-D through each episode, and that tracking has one systematic failure mode on this task: the moment the gripper stops occluding the nut, the pose can snap. Measured across all 30 episodes, every one carries 2–6 frames whose pose moves more than 3 cm in a single 15 Hz step (up to 15 cm), and after the gripper opens the pose can drift up to 7.9 cm even though the nut is sitting still on the peg.

Those frames are not merely noisy, they are wrong, and a wrong auxiliary target is worse than a missing one: it pushes gradient into the shared visual encoder for an input that does not support the label. This is the same failure the simulated study handles for coffee_pod yaw with ROTATION_BLOCKS — supervising a quantity the image cannot determine turns the auxiliary head into a noise source rather than a regulariser.

How the mask was derived

From two physical facts about the task, with no hand labelling:

1. Between consecutive frames the nut cannot move 3 cm. At 15 Hz that is 0.45 m/s, far above the teleoperation speeds actually used (measured maximum end-effector step was 7–10 mm). A larger jump means the pose teleported, not the object.
2. The nut is static before the gripper first closes and after it finally opens. Within those windows, any deviation from the window's median position is measurement error; the threshold is 2 cm.

Frames within ±2 of a flagged frame are also marked invalid: a jump means the pose was already sliding on the way in. The exact procedure is aux_validity() in panda_utils/scripts/r2st_to_robomimic.py.

How to use it

Per-frame auxiliary loss, masked and renormalised by the number of valid frames rather than the batch size:

m = batch["obs"]["aux_valid"].float()                     # (B, T)
per_frame = ((pred - target) ** 2).mean(-1)               # (B, T)
aux_loss = (per_frame * m).sum() / m.sum().clamp(min=1)

Add aux_valid to observation.modalities.obs.low_dim in the training config so the data loader actually reads it — robomimic only loads the observation keys the config names, so without that line the key sits in the file unused.

Ignoring the mask is a valid first run. Training works without any of this; the auxiliary head simply receives 2.4% corrupted targets. Wire the gating in if the auxiliary arms underperform the baseline.

Two things that will bite you

There is no simulator behind this data. data.attrs["env_args"] records provenance only; the environment named there does not exist. Set experiment.rollout.enabled = false in the training config, or robomimic will try to construct a rollout environment from it and fail. This also means the study's eval_fixed_scenes protocol does not apply — real-robot evaluation is a separate problem.

Actions are reconstructed, not recorded. The demonstration logger (panda_utils/scripts/log_demonstration2.py, and the ROS 1 original it was ported from) builds a teleoperation action with input2action, sends it to the controller, and never stores it. Actions here are therefore derived from what the arm actually did:

dpos  = eef_pos[t+1] - eef_pos[t]              robot base frame
drot  = axis-angle of R[t].T @ R[t+1]          as OSC_POSE expects
grasp = +1 while the fingers are closed, -1 while open

For behaviour cloning that is arguably the better label — the policy should imitate the motion that happened, not a command the impedance controller only partly tracked — but it is not the original command, and the final step repeats the previous action because no t+1 exists for it.

Calibration and accuracy

Camera-to-robot extrinsics come from a ChArUco hand-eye calibration (panda_utils/scripts/r2st_handeye.py): the board's pose in the camera from PnP, its pose in the robot base frame from touching five board corners with the closed fingertips and reading forward kinematics. Residual 2.58 mm RMS over those five points; the fitted board plane is 0.67° from level and sits at z = +0.3 mm, consistent with the arm being bolted to that table.

Known residual error sources:

• D435 depth reads ~14 mm farther than RGB geometry on this white table at 0.86 m (measured against the ChArUco PnP solution over 7 corners, std 1.7 mm). Systematic, not noise. It does not affect the extrinsics, which use RGB only, but FoundationPose consumes depth, so object positions carry roughly that bias along the camera ray.
• Mesh proportions are approximate. The nut and peg meshes are parametric placeholders; scale_glb_to_pointcloud rescaled them 1.40x and 2.13x onto the observed point clouds. Physical measurements would improve pose accuracy.
• Forward-kinematics cross-check at the grasp frame puts the tracked nut 15–41 mm from panda_hand_tcp horizontally across the 30 episodes. Most of that is geometric — the gripper holds the handle, while the tracked pose is the mesh origin — and its consistency suggests a systematic offset rather than random error.

Object placement is anisotropic

Initial nut position in the robot base frame, over the 30 episodes:

axis	range	std
x (toward/away from the robot)	6.2 cm, in two clusters	2.2 cm
y (left/right)	20.9 cm	6.4 cm

A 3.3 : 1 anisotropy, and x is really two rows rather than a spread. Anything that measures generalisation as a function of distance from the training distribution has almost no in-distribution range along x to measure against. Spread x if you extend this dataset.