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THU98/
pi05_franka_5task_full_20k

Name: pi05_franka_5task_full_20k
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π0.5 Dual-Franka Five-Task Full SFT — 20k

English | 中文说明

This repository contains the final JAX/Orbax checkpoint from full-parameter supervised fine-tuning of the official OpenPI pi05_base model on five real-robot manipulation tasks collected with a dual Franka FR3 setup.

Tasks and prompts

Task ID	Training prompt
`place_bowl_on_cabinet`	`Place the green bowl on top of the cabinet with the left arm.`
`place_bowl_on_plate_pt2`	`Place the green bowl on the blue plate with the left arm.`
`place_bowl_on_plate`	`Place the gray bowl on the green plate with the left arm.`
`place_cup_on_tray`	`Place the cup on the tray with the left arm.`
`tape_on_basket`	`Place the tape in the green basket with the left arm.`

Use the exact prompt wording above for the closest match to training-time conditioning.

Data

Platform: dual Franka FR3, joint-space control
Source: real-robot teleoperation trajectories
Raw episodes: 250 (50 per task)
Training split: 225 episodes (45 per task)
Held-out split: 25 episodes (5 per task)
Cameras: chest, left wrist, right wrist
Robot state: 16 dimensions — left 7 joints + left gripper + right 7 joints + right gripper
Source action: the same 16-dimensional dual-arm joint/gripper ordering
Source streams were aligned at 20 Hz and stored in LeRobot format

The OpenPI model configuration pads the 16-dimensional source action to action_dim=32 internally and predicts an action horizon of 20.

Data-quality note

For place_bowl_on_plate_pt2, 32 trajectories had chest-camera timestamp offsets above the strict default conversion threshold. They were retained with a 3000 ms camera-alignment tolerance to satisfy the experiment requirement of using all 50 trajectories. This can introduce observation/action temporal mismatch and should be considered when interpreting real-robot behavior.

Training configuration

Setting	Value
Base checkpoint	Official OpenPI `pi05_base`
Method	Full-parameter SFT
Training steps	20,000
Hardware	1 × NVIDIA A100 80GB
Per-device / global batch	2 / 2
Optimizer	AdamW, gradient clipping at 1.0
Learning rate	cosine schedule, 1,000 warmup steps, peak `1e-5`, floor `1e-6`
EMA	Disabled
Checkpoint interval	1,000 steps
Loss logging	Every 100 steps
Final checkpoint index	`19999`

The last logged training loss at step 19,900 was 0.0096. Training loss is not a real-robot success metric.

Download and loading

bash

hf download THU98/pi05_franka_5task_full_20k \
  --local-dir ./pi05_franka_5task_full_20k

The repository is an OpenPI JAX/Orbax checkpoint. Use it with a matching local OpenPI configuration named pi05_franka_5task_pt2_full:

bash

uv run scripts/serve_policy.py policy:checkpoint \
  --policy.config=pi05_franka_5task_pt2_full \
  --policy.dir=./pi05_franka_5task_full_20k

The local configuration must match the training architecture (pi05=True, action_dim=32, action_horizon=20) and must supply the included normalization assets. This is not a Transformers from_pretrained() checkpoint.

Evaluation status

No controlled real-robot success-rate evaluation is published with this checkpoint. The 25 held-out episodes were retained for offline checkpoint comparison, but training loss alone does not establish deployment performance.

Intended use and limitations

Research use on a dual Franka setup with matching cameras, joint order, gripper convention, preprocessing, normalization, control rate, and prompts.
Not validated for other robots, camera calibration, viewpoints, objects, or language paraphrases.
The five tasks are narrow and mostly left-arm behaviors; this is not a general-purpose robot policy.
Before hardware deployment, verify action bounds, emergency stops, collision avoidance, workspace limits, latency, and low-speed dry runs.
Human supervision and independent safety controls are required. The model must not directly bypass robot safety systems.

中文说明

这是基于官方 OpenPI pi05_base，使用双 Franka FR3 真机遥操数据进行全参数 SFT 得到的五任务模型。训练集为 225 条、留出集为 25 条，总训练 20,000 步。输入包括胸部、左腕、右腕三路图像、Language Instruction 和 16 维双臂关节/夹爪状态；输出为双臂动作序列。

place_bowl_on_plate_pt2 中部分轨迹存在较大的胸部相机时间戳偏差。本实验为保留全部 50 条数据放宽了对齐阈值，因此部署时需要重点检查视觉与动作的时间同步。当前没有发布正式真机成功率，训练 loss 不能替代真机评测。

Sources

OpenPI: https://github.com/Physical-Intelligence/openpi