pi05_franka_haply_abscartesianpos

pi05_franka_haply_abscartesianpos

π₀.₅ fine-tuned from lerobot/pi05_base on Ameyapores/franka_haply_joint_delta, predicting absolute end-effector position and gripper.

Checkpoint taken at the step with the lowest eval loss: step 1,750, eval_loss 0.1120.

Action space

The policy outputs a 4-dimensional action, and the order is part of the contract:

These are absolute positions, not displacements. Feeding the output to a controller that expects a delta will drive the arm to the workspace origin. Observed ranges in the training data are x ∈ [0.35, 0.66], y ∈ [−0.16, 0.19], z ∈ [0.13, 0.49] m — a Franka envelope, not increments.

The source column is action_cartesian_absolute. Note the dataset's default action column holds joint deltas and action_cartesian holds delta xyz despite the similar name; this model was trained on neither.

Observations

Three 224×224 RGB cameras, each a single frame — π₀.₅ uses no observation history (n_obs_steps=1), so all temporal structure comes from predicting a chunk forward:

• observation.images.base_0_rgb
• observation.images.base_1_rgb
• observation.images.left_wrist_0_rgb

plus an 8-dim observation.state and a language prompt. The dataset is recorded at 20 fps, so chunk_size=50 is 2.5 s of motion.

Training

eval_loss is the flow-matching objective on held-out episodes — not a success rate. The holdout is 10 episodes, so differences of a few percent are not resolved.

Comparison

All arms below share this base, chunk, optimiser and the same 84/10 holdout, so the losses are directly comparable. Only the action encoding differs.

The last row is the informative one: identical in every respect except absolute versus incremental position, and 47% worse. With a single observation frame, a delta is a rate regressed from a snapshot, while an absolute target is directly readable from the current image and state. Absolute encodings occupy the top of this table; incremental ones the bottom.

Usage

from lerobot.policies.pi05.modeling_pi05 import PI05Policy

policy = PI05Policy.from_pretrained("arkojit1/pi05_franka_haply_abscartesianpos")

Caveats

• One seed, one task, one operator. The 0.0004 gap to the sliced-column variant is noise, not a ranking.
• Trained on Haply-teleoperated demonstrations of a single language task; no claim is made about transfer to other tasks or robots.
• Evaluated by held-out loss only. No rollout success rate has been measured.