PINlab/pi05-so101

pi0.5 SO-101 with a tactile skin

pi0.5 policies for a single SO-101 arm with a 5-chip magnetic tactile skin, fine-tuned from pi05_base. One folder per training run, one sub-folder per checkpoint step. None has been evaluated on the robot yet.

What is here

folder	config	cameras	prompt(s)	data	steps
grab_ball_skin/	pi05_so101_grab_ball_skin	2: base = fixed (train: fixed/side at random), wrist = handeye	"Grab the ball"	endoard/grab_ball_3cam_skin, first 100 episodes / 46,076 frames	10000, 20000, 29999
grab_ball_skin_3cam/	pi05_so101_grab_ball_skin_3cam	3: fixed, handeye, side	"Grab the ball"	endoard/grab_ball_3cam_skin, 150 episodes / 65,989 frames	10000, 20000, 29999
grab_cup_skin_3cam/	pi05_so101_grab_cup_skin_3cam	3: fixed, handeye, side	"Grab the blue cup"	endoard/grab_cup_3cam_skin, 166 episodes / 64,812 frames	10000, 20000, 29999
grab_3obj_skin_3cam/	pi05_so101_grab_3obj_skin_3cam	3: fixed, handeye, side	"Grab the red cube" / "Grab the green cube" / "Grab the green cylinder"	endoard/grab_{red_cube,green_cube,green_cylinder}_3cam_skin merged, 450 episodes / 161,918 frames, one task per source	10000, 20000, 28000

grab_3obj_skin_3cam is multi-task: the prompt selects the object. Its run was stopped at step 28,500 of 30k, so 28000 is its last checkpoint. Its held-out split is stratified per task (15 episodes each).

Each step folder is an openpi checkpoint without optimizer state: params/ (EMA weights), assets/<asset_id>/norm_stats.json (quantile stats), _CHECKPOINT_METADATA, and a README.md with that checkpoint's details.

Download one checkpoint

from huggingface_hub import snapshot_download
d = snapshot_download("PINlab/pi05-so101", allow_patterns="grab_cup_skin_3cam/29999/*")
# serve with --policy.dir={d}/grab_cup_skin_3cam/29999

Serving

The checkpoints carry no config. Serve each one under the config in the table, which pins its asset_id:

python scripts/serve_policy.py policy:checkpoint \
    --policy.config=pi05_so101_grab_cup_skin_3cam \
    --policy.dir=<download>/grab_cup_skin_3cam/29999

The policy returns 16 actions spaced 2 frames apart (15 Hz), absolute joints (the delta is undone server-side). Replay at 15 Hz or interpolate; replaying at 30 Hz halves the speed. The 3-camera policies need all three cameras every step.

Recipe (all runs)

• State 21-D (padded to 32): 5 joints (deg), gripper (0-100), 5 skin chips x xyz (uT), raw.
• Action 6-D (padded to 32): delta on the 5 joints, absolute gripper (make_bool_mask(5, -1)).
• Images: base_0_rgb = fixed, left_wrist_0_rgb = handeye; right_wrist_0_rgb = side (mask on) for 3-camera runs, zeros with mask off for grab_ball_skin.
• action_horizon=16, action_stride=2, prompt from the dataset task string.
• From pi05_base, 4 nodes x 1 A100-64GB, --fsdp-devices=4, batch 32, AdamW with cosine decay, EMA 0.99, seed 42, 30k steps, 10% of episodes held out.