Robotics
LeRobot
Safetensors
pi05
vision-language-action
imitation-learning
ur7e

Model Card for Ο€0.5 β€” UR7e PickandPlace (30 epoch)

Ο€β‚€.β‚… (Pi05) Policy

Ο€β‚€.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 Ο€β‚€.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‰ˆ 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 은 7 이며, Ο€0.5 의 max_action_dim=32, max_state_dim=32 으둜 μžλ™ zero-pad λ©λ‹ˆλ‹€.


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 의 카메라 ν‚€λŠ” ν•™μŠ΅ μ‹œ μ‚¬μš©ν•œ 이름(`observation.images.base_0_rgb`,
# `observation.images.left_wrist_0_rgb`) κ³Ό 동일해야 ν•©λ‹ˆλ‹€.
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

원본 ν•™μŠ΅ μŠ€ν¬λ¦½νŠΈλŠ” scripts/cap/pi05_cap_ur7e_pickandplace.sh 이며, μ •ν™•ν•œ hyperparameter λŠ” 이 리포의 train_config.json μœΌλ‘œλ„ μž¬κ΅¬μ„± κ°€λŠ₯ν•©λ‹ˆλ‹€.


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