README.md

metadata

library_name: lerobot
pipeline_tag: robotics
base_model: lerobot/pi05_base
base_model_relation: finetune
datasets:
  - CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps
license: other
tags:
  - lerobot
  - pi05
  - pi0.5
  - openpi
  - vision-language-action
  - imitation-learning
  - robotics
  - ur7e
  - arrange-block
  - 10fps
  - safetensors

CoRL2026-CSI/Pi0.5-UR7e-ArrangeBlock_30epoch

This is a LeRobot PI0.5 policy fine-tuned from lerobot/pi05_base on CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps.

The model is intended for UR7e ArrangeBlock manipulation experiments using RGB observations, robot proprioception, language instructions, and continuous action chunks. It is uploaded as a LeRobot policy checkpoint and should be loaded through the matching LeRobot PI0.5 implementation used for training.

Model Details

• Policy type: PI0.5
• Base policy: lerobot/pi05_base
• PaliGemma variant: gemma_2b
• Action expert variant: gemma_300m
• Action chunk size: 16
• Action steps: 16
• Max state/action dims: 32 / 32
• Vision encoder frozen: false
• Train expert only: false
• Gradient checkpointing: true
• Training dtype: bfloat16

Fine-Tuning Setup

• Dataset: CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps
• Training steps: 5550
• Approx. epochs: 30.16
• Final training samples: 1420800
• Final training loss: 0.009152
• Runtime: 19.12 hours
• Per-GPU batch size: 64
• Gradient accumulation steps: 2
• Number of GPUs: 2
• Effective batch size: 256
• Optimizer lr: 2.5e-05
• Optimizer betas: [0.9, 0.95]
• Weight decay: 0.01
• Scheduler warmup/decay: 1000 / 30000
• Final decay lr: 2.5e-06
• DataLoader workers: 8
• DataLoader prefetch factor: 1

Camera Mapping

• no explicit rename map

Image Augmentation

• disabled

Inputs

• observation.images.base_0_rgb: STATE, shape [1]
• observation.images.left_wrist_0_rgb: STATE, shape [1]
• observation.images.realsense_topview: VISUAL, shape [3, 480, 640]
• observation.images.realsense_wrist: VISUAL, shape [3, 480, 640]
• observation.images.right_wrist_0_rgb: STATE, shape [1]
• observation.state: STATE, shape [7]

Outputs

• action: ACTION, shape [7]

Usage

Install and use the same LeRobot checkout/environment that contains the PI0.5 policy implementation, then point policy.path to this Hub repo.

lerobot-record \
  --robot.type=<your_robot> \
  --dataset.repo_id=<your_eval_dataset_repo> \
  --policy.path=CoRL2026-CSI/Pi0.5-UR7e-ArrangeBlock_30epoch \
  --episodes=10

For local Python usage, load the policy with LeRobot's policy factory from the training checkout.

Evaluation

This upload records the offline training run metrics only. No rollout success rate is claimed here unless a separate real or simulated evaluation is added later.

Final logged training metrics:

• loss: 0.009152
• grad norm: 0.377971
• learning rate: 2.500051366567086e-06
• update time: 6.0738 s/step
• dataloading time: 0.0210 s/step

Limitations and Safety

This model is a robot control policy and can produce unsafe actions if deployed on hardware without appropriate validation, workspace limits, emergency stop handling, and task-specific safety checks. Test in simulation or a constrained setup before any physical deployment.

The model is specialized to the training dataset, camera mapping, calibration, action space, and embodiment configuration. It may not transfer reliably to different robots, camera placements, object layouts, or tasks without further validation or fine-tuning.

License and Terms

The training dataset is marked apache-2.0. This fine-tuned model is conservatively marked as other; users are responsible for checking the applicable base model, dataset, and deployment terms before use.

Files

• model.safetensors: fine-tuned policy weights
• config.json: LeRobot PI0.5 policy config
• train_config.json: training configuration
• policy_preprocessor.json and policy_postprocessor.json: LeRobot processor pipelines
• policy_*_processor.safetensors: normalization/statistics state used by processors