---
license: apache-2.0
tags:
  - robotics
  - vla
  - dreamzero
  - lora
  - arx_x5
base_model: Luminis-Sim/DreamZero-AgiBot
---

# DreamZero — Epoch 4 checkpoint

LoRA fine-tune of [DreamZero-AgiBot](https://huggingface.co/Luminis-Sim/DreamZero-AgiBot) on the RoboDojo arx_x5 dataset (6-DOF dual-arm).

- Checkpoint step: **35,000** (epoch 4)
- Action: relative for arm/head/waist, absolute for grippers
- Base model: DreamZero-AgiBot (7-DOF AgiBot G1 pretrain)

## Training loss

![loss curves](./loss_curves.png)

Red dashed lines mark epoch boundaries (step 8,750 / 17,500 / 26,250 / 35,000).

| step | total | arm | ee (gripper) | aux |
|---:|---:|---:|---:|---:|
| 100 | 0.418 | 0.427 | 1.037 | 0.026 |
| 500 | 0.288 | 0.255 | 0.979 | 0.016 |
| 1,000 | 0.238 | 0.199 | 0.513 | 0.005 |
| 2,000 | 0.200 | 0.158 | 0.179 | 0.011 |
| 3,500 (warmup end) | 0.169 | 0.201 | 0.175 | 0.002 |
| 5,000 | 0.140 | 0.137 | 0.124 | 0.002 |
| **8,750 (epoch 1)** | 0.109 | 0.154 | 0.178 | 0.011 |
| 12,000 | 0.098 | 0.057 | 0.097 | 0.004 |
| **17,500 (epoch 2)** | 0.118 | 0.051 | 0.033 | 0.001 |
| 22,000 | 0.118 | 0.144 | 0.085 | 0.001 |
| **26,250 (epoch 3)** | 0.087 | 0.115 | 0.061 | 0.002 |
| 30,000 | 0.087 | 0.062 | 0.049 | 0.013 |
| **35,000 (epoch 4)** | **0.084** | **0.081** | **0.039** | **0.003** |

Summary (step 0 → 35,000):
- Total loss: 0.42 → **0.08** (−80%)
- Arm loss: 0.43 → **0.08** (−81%)
- Gripper loss: 1.04 → **0.04** (−96%)
- Aux loss: 0.03 → **0.003** (live-channel mask working)

## Files
| File | Purpose |
|---|---|
| `model.safetensors` | LoRA weights (317 MB) |
| `experiment_cfg/conf.yaml` | Full Hydra training config (required at inference) |
| `config.json` | HF Trainer config |
| `trainer_state.json` | Per-step loss history |
| `loss_curves.png` | Loss visualization |

## Bug fixes vs original DreamZero training

Includes the fixes shipped in [Luminis-Sim/XPolicyLab#31](https://github.com/Luminis-Sim/XPolicyLab/pull/31):
1. J3-locked joint routing for 6-DOF arms onto AgiBot G1's 7-DOF slot layout
2. Mask-aware action loss (live-channel detection)
3. Gripper normalization q99 → min_max
4. Per-modality (arm/ee/aux) loss logging