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pi05_teleop_open_drawer

Name: pi05_teleop_open_drawer
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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:

bash

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:

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

Cache-SCA/pi05_teleop_open_drawer