---
license: apache-2.0
tags:
  - robotics
  - vla
  - pi0.5
  - openpi
  - real-robot
  - franka
  - mode-editing
---

# pi05_real_pb_from_right

Fine-tuned [pi0.5](https://github.com/Physical-Intelligence/openpi) VLA model for real robot manipulation.

## Task
- **Task:** Push Block
- **Training data:** From-right mode only
- **Dataset:** `real_push_block_from_right`
- **Robot:** Franka Panda (7-DOF)
- **Cameras:** Base RGB + Wrist RGB (256x256)

## Training Configuration
| Parameter | Value |
|-----------|-------|
| Base model | pi0.5 (PaliGemma 2B + Gemma 2B action expert) |
| Total parameters | ~3.35B |
| Action dimension | 32 |
| Action horizon | 10 |
| Batch size | 16 |
| Training steps | 5,000 |
| Learning rate | Cosine decay: warmup=500, peak=5e-5, end=5e-6 |
| Optimizer | AdamW (gradient clip norm=1.0) |
| GPUs | 8x NVIDIA A100 |
| Normalization | Quantile normalization |

## Checkpoints
- **Step 3000**: loss = 0.0060
- **Step 4000**: loss = 0.0040
- **Step 4999**


## Loss Curve

| Step | Loss |
|------|------|
| 0 | 0.0835 |
| 500 | 0.0154 |
| 1000 | 0.0125 |
| 1500 | 0.0105 |
| 2000 | 0.0084 |
| 2500 | 0.0070 |
| 3000 | 0.0060 |
| 3500 | 0.0051 |
| 4000 | 0.0040 |
| 4500 | 0.0035 |


## Part of Mode Editing Research
This checkpoint is part of the "Don't Filter Your Data, Edit Your Policy" project (CoRL 2026),
investigating post-hoc behavior mode editing for robot policies using Classifier-Guided Distillation (CG-Distill).