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pi05_franka_haply_joint_delta
arkojit1/pi05_franka_haply_joint_delta
pi05_franka_haply_joint_delta is a robotics model from arkojit1. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for lerobot.
π₀.₅ fine-tuned on Haply-teleoperated Franka demonstrations, predicting joint-space deltas. Checkpoint taken at the step with the lowest eval loss.
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Model card
From the Hugging Face model README
π₀.₅ — joint-delta control on franka_haply_joint_delta
π₀.₅ fine-tuned on Haply-teleoperated Franka demonstrations, predicting joint-space deltas. Checkpoint taken at the step with the lowest eval loss.
| Base checkpoint | lerobot/pi05_base |
| Dataset | Ameyapores/franka_haply_joint_delta — 94 episodes / 50,861 frames @ 20 fps, 1 language task |
| Step | 3,500 of a 5,000-step run (19.6 epochs) |
| Eval loss | 0.1977 (flow-matching objective, 10% held-out episodes) |
| Trainable | action expert only (--train_expert_only: SigLIP and Gemma-2B frozen, ~300M trainable) |
Action space
8-dimensional, from the dataset's action column:
| dims | meaning |
|---|---|
| 0–6 | per-joint angle deltas (7 DoF) |
| 7 | gripper |
Observations are three 224×224 RGB cameras (observation.images.base_0_rgb,
observation.images.base_1_rgb, observation.images.left_wrist_0_rgb) plus an 8-dim
observation.state. empty_cameras=0 — the three cameras exactly fill π₀.₅'s three image
slots, with no masked padding.
chunk_size=50 and n_action_steps=50 are pi05_base's own defaults. At 20 fps that is
2.5 s of predicted motion, executed open-loop in full before re-planning. If you want
tighter closed-loop control, lower n_action_steps at inference; it does not affect the
weights.
Usage
from lerobot.policies.pi05.modeling_pi05 import PI05Policy
policy = PI05Policy.from_pretrained("arkojit1/pi05_franka_haply_joint_delta")
Requires access to the gated google/paligemma-3b-pt-224
repo — the preprocessor loads its tokenizer by id. Accept the license there first and make
sure HF_TOKEN is set.
Training
Global batch 256 across 8×MI300X (RCCL DDP, 32 per GPU), learning rate 2.5e-5 with a cosine schedule, bf16 with gradient checkpointing, quantile normalisation of state and action, image augmentation on the train split only.
eval_loss is the training objective evaluated on held-out episodes — not a success rate.
- Task
- Robotics
- Library
- lerobot
- Created
- Sep 21, 2026
- Updated
- Sep 21, 2026