Instructions to use CoRL2026-CSI/pi05_teleop_close_pot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use CoRL2026-CSI/pi05_teleop_close_pot with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
tags:
- lerobot
- robotics
- pi05
- so101
- imitation-learning
datasets:
- CoRL2026-CSI/SO101-teleop_close_pot_lid_100epi
base_model: lerobot/pi05_base
π0.5 — SO-101 close_pot_lid
Fine-tuned lerobot/pi05_base on 100 teleop episodes of the SO-101 close_pot_lid task.
Model
- Architecture: π0.5 (PaliGemma-2B VLM + Gemma-300M action expert, flow matching, 10 inference steps)
- Cameras:
base_0_rgb,left_wrist_0_rgb,right_wrist_0_rgb(224×224) - State / Action dim: 32 (padded) / 6 (SO-101)
- Action chunk: 50
- dtype: bfloat16
Camera key rename (dataset → policy):
observation.images.top → observation.images.base_0_rgb
observation.images.wrist → observation.images.left_wrist_0_rgb
right_wrist_0_rgb is an empty camera slot for this single-arm setup.
Action features (SO-101): shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper (.pos).
Normalization: ACTION/STATE = MEAN_STD, VISUAL = IDENTITY.
Data
CoRL2026-CSI/SO101-teleop_close_pot_lid_100epi — 100 episodes, 57,173 frames, human teleop.
Training
| Hardware | 4 × GPU (DDP, 🤗 Accelerate) |
| Per-device batch | 32 |
| Gradient accumulation | 2 |
| Effective global batch | 256 |
| Steps | 11,200 (~50 epochs) |
| Optimizer | AdamW, β=(0.9, 0.95), wd=0.01, grad clip 1.0 |
| LR | cosine decay, peak 2.5e-5 → 2.5e-6, warmup 1000, decay 30000 |
| Gradient checkpointing | on |
| Image aug | ColorJitter (brightness/contrast/saturation/hue), SharpnessJitter, RandomAffine — max_num=3, random order |
| Seed | 1000 |
Training script: scripts/train_pi05_close_pot_lid.sh.
Usage
from lerobot.policies.pi05.modeling_pi05 import PI05Policy
policy = PI05Policy.from_pretrained("CoRL2026-CSI/pi05_close_pot").to("cuda").eval()
lerobot-eval --policy.path=CoRL2026-CSI/pi05_close_pot --env.type=<env> --eval.n_episodes=20
Limitations
- Single task, single seed; no quantitative success rate reported here.
- Trained on a single-arm SO-101; the right-wrist camera slot is empty.
- 100 episodes only — sensitive to camera/lighting domain shift.
License
Apache 2.0 (inherits from lerobot/pi05_base).