README.md

metadata

library_name: lerobot
base_model: lerobot/pi05_base
datasets:
  - CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi
tags:
  - lerobot
  - robotics
  - robot-learning
  - imitation-learning
  - behavior-cloning
  - visuomotor-policy
  - vision-language-action
  - pi05
  - pi0.5
  - openpi
  - paligemma
  - so101
  - teleoperation
  - open-drawer
  - pytorch

Pi0.5 Teleop Open Drawer

This repository contains a LeRobot Pi0.5 policy fine-tuned for the SO101 open_drawer teleoperation task.

The checkpoint was fine-tuned from lerobot/pi05_base on CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi and saved after the final training step.

Model Details

• Policy type: pi05
• Base model: lerobot/pi05_base
• Training dataset: CoRL2026-CSI/SO101-Teleop-Open_drawer_100epi
• Task: open a drawer with SO101 teleoperation demonstrations
• Checkpoint: final checkpoint at step 2200
• Action dimension: 6
• State dimension: 32
• Image resolution: 224 x 224
• Precision: bfloat16
• Training framework: LeRobot

Input and Output Features

The policy checkpoint is configured with the following observation features:

• observation.images.base_0_rgb: visual input, shape [3, 224, 224]
• observation.images.left_wrist_0_rgb: visual input, shape [3, 224, 224]
• observation.images.right_wrist_0_rgb: visual input, shape [3, 224, 224]
• observation.state: robot state, shape [32]

The output feature is:

• action: robot action, shape [6]

The saved policy preprocessor maps dataset camera keys as follows:

• observation.images.top -> observation.images.base_0_rgb
• observation.images.left_wrist -> observation.images.left_wrist_0_rgb

Training

Training used the following main settings:

• Steps: 2200
• Batch size: 32
• Gradient accumulation: 4
• Optimizer: AdamW
• Learning rate: 2.5e-5
• Scheduler: cosine decay with warmup
• Image augmentation: enabled
• Final training loss: 0.0379133597
• Final train steps logged: 2200
• Final train samples logged: 140800
• Final train epochs logged: 6.2694808086

No separate evaluation results are included in this repository.

Usage

Use the model as a LeRobot policy by pointing --policy.path at this Hub repo:

lerobot-record \
  --robot.type=<your_robot_type> \
  --dataset.repo_id=<your_eval_dataset_repo> \
  --policy.path=CoRL2026-CSI/pi05_teleop_open_drawer \
  --episodes=10

You can also load it directly in Python:

from lerobot.policies.pi05.modeling_pi05 import PI05Policy

policy = PI05Policy.from_pretrained("CoRL2026-CSI/pi05_teleop_open_drawer")
policy.eval()

Files

• model.safetensors: policy weights
• config.json: Pi0.5 policy configuration
• train_config.json: training configuration
• policy_preprocessor.json: saved policy input processor pipeline
• policy_postprocessor.json: saved policy output processor pipeline
• *_processor.safetensors: normalization and unnormalization state