Instructions to use CoRL2026-CSI/pi05-UR7e-PickandPlace-30epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use CoRL2026-CSI/pi05-UR7e-PickandPlace-30epoch with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Model Card for Ο0.5 β UR7e PickandPlace (30 epoch)
Οβ.β (Pi05) Policy
Οβ.β is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository. See the Physical Intelligence Οβ.β blog post.
This checkpoint is a fine-tune of lerobot/pi05_base
on the CoRL2026-CSI/UR7e_CaP_PickandPlace_100epi_10fps
dataset for a UR7e single-arm pick-and-place task.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
Training Summary
| Field | Value |
|---|---|
| Base model | lerobot/pi05_base |
| Dataset | CoRL2026-CSI/UR7e_CaP_PickandPlace_100epi_10fps (100 eps, 35,878 frames, 10 fps) |
| Robot | UR7e single-arm, 7-DoF (6 joints + gripper) |
| Cameras | realsense_topview, realsense_wrist (renamed β base_0_rgb/left_wrist_0_rgb) |
| Steps | 4,300 (β 30 epoch Β· 35878 Γ 30 / 256) |
| Batch | 32 Γ 2 GPU Γ 4 grad_accum = 256 per optimizer-step samples |
| VLM / Action expert | PaliGemma gemma_2b / gemma_300m, bfloat16 |
| Optimizer | AdamW (lr 1e-4, betas (0.9, 0.95), wd 1e-10), cosine decay w/ warmup 1000 |
| Chunk / Action steps | 50 / 50 |
| Memory | gradient_checkpointing=true, compile_model=false |
| Normalization | ACTION/STATE = MEAN_STD, VISUAL = IDENTITY |
| Image augmentation | brightness, contrast, saturation, hue, sharpness, affine (max 3, random order) |
| Hardware | 2Γ NVIDIA RTX PRO 6000 Blackwell |
action/observation.state dim μ 7 μ΄λ©°, Ο0.5 μ max_action_dim=32, max_state_dim=32 μΌλ‘ μλ zero-pad λ©λλ€.
How to Get Started
Inference (load + step)
import torch
from lerobot.policies.pi05.modeling_pi05 import PI05Policy
policy = PI05Policy.from_pretrained("CoRL2026-CSI/pi05-UR7e-PickandPlace-30epoch")
policy.to("cuda").eval()
# observation μ μΉ΄λ©λΌ ν€λ νμ΅ μ μ¬μ©ν μ΄λ¦(`observation.images.base_0_rgb`,
# `observation.images.left_wrist_0_rgb`) κ³Ό λμΌν΄μΌ ν©λλ€.
with torch.inference_mode():
action = policy.select_action(observation)
Continue fine-tuning
lerobot-train \
--policy.path=CoRL2026-CSI/pi05-UR7e-PickandPlace-30epoch \
--dataset.repo_id=CoRL2026-CSI/UR7e_CaP_PickandPlace_100epi_10fps \
--output_dir=outputs/train/pi05_ur7e_pickandplace_ft \
--job_name=pi05_ur7e_pickandplace_ft \
--batch_size=32 --gradient_accumulation_steps=4 --steps=1000 \
--policy.device=cuda --policy.dtype=bfloat16 \
--policy.gradient_checkpointing=true --wandb.enable=true
μλ³Έ νμ΅ μ€ν¬λ¦½νΈλ scripts/cap/pi05_cap_ur7e_pickandplace.sh μ΄λ©°,
μ νν hyperparameter λ μ΄ λ¦¬ν¬μ train_config.json μΌλ‘λ μ¬κ΅¬μ± κ°λ₯ν©λλ€.
Model Details
- License: apache-2.0
- Base model:
lerobot/pi05_base - Library: LeRobot
- Trained by: CoRL2026-CSI
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Model tree for CoRL2026-CSI/pi05-UR7e-PickandPlace-30epoch
Base model
lerobot/pi05_base