oku006/bussing-pi05-droid-ownstats

π0.5-DROID fine-tuned on the bussing task — own normalization stats

Same as oku006/bussing-pi05-droid except the normalization statistics are computed from the bussing episodes instead of reused from DROID.

Measured at the same horizon, the two are near-identical and the DROID-stats version is marginally ahead (see the table below). The motivation for trying this was that the bussing teleoperation is ~5× slower than DROID's average (joint velocity std 0.03–0.05 vs 0.15–0.31), so DROID's normalization compresses our action range. In practice that did not translate into better predictions — openpi's advice to reuse the pretrained statistics holds up.

Kept public because it is a genuine alternative worth trying on hardware: it commands slightly more motion (0.928 vs 0.921), and offline metrics do not settle which behaves better in a closed loop.

Deploy

hf download oku006/bussing-pi05-droid-ownstats --local-dir ./ckpt
# paste train_config_snippet.py into openpi/src/openpi/training/config.py
python scripts/serve_policy.py \
    --policy.config=pi05_droid_bussing_ownstats \
    --policy.dir=./ckpt

policy_config:
  model_config:
    model_id: pi05_droid_bussing_ownstats   # contains "droid", passes the assert
    checkpoint_dir: /path/to/ckpt
  flip_gripper: False        # <-- trained on the raw recorded convention
  action_space: joint_velocities

flip_gripper must be False. The flip converts this robot's gripper convention into DROID's for the stock model; this checkpoint was fine-tuned on the raw recorded values (1.0 = open, ~0.3 = closed), so flipping inverts it.

Interface

Standard DROID contract, no conversion step.

observations	exterior_image_1_left, wrist_image_left, joint_position (7), gripper_position (1), prompt
actions	8-d: 7 joint velocities (rad/s) + gripper position
action horizon	16, first 8 executed

Measured — held-out episodes [2, 4, 10, 20, 21], 8-step horizon

All three checkpoints, identical protocol — same 5 held-out episodes, same 48 samples, same 8-step horizon:

metric	this (own stats)	DROID stats	ACT
velocity error (rad/s)	0.0190	0.0187	0.0203
beats zero-motion baseline	28.9%	29.9%	24.1%
gripper error	0.0097	0.0073	0.0349
motion ratio (1.0 = human)	0.928	0.921	0.940
joint error, 8 executed steps	0.0289	0.0286	0.0307
clipped by controller	2.8%	1.8%	2.1%

Both π0.5 variants clearly beat the ACT policy on the gripper channel — ~4× lower error — which matters directly for whether a grasp closes at the right moment. Between the two π0.5 variants the gap is under one point on every metric, i.e. effectively a tie.

Limits

33 of 38 episodes used for training, 5 held out. One task. No generalization to new objects or layouts expected. All recordings start from ≈ the same home pose (1 cm spread across all 38), so starting far from it is out of distribution.