Downloads · 30 days
0
pi05-arx5-multitask-v1
pravsels/pi05-arx5-multitask-v1
pi05-arx5-multitask-v1 is a robotics model from pravsels. Use it for the robotics task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
PI0.5 fine-tuned on 186 ARX5 single-arm and bimanual manipulation datasets. Supports mixed 7D (single-arm) and 14D (bimanual) action spaces with per-dimension loss masking.
Access
Public
Updated Mar 24, 2026
Repo size
37.3 GB
Likes
1
Public
Repo makeup
Click a slice to open those files.
Other37.3 GB · 100%
At a glance
- Task
- Robotics
- License
- apache-2.0
- Access
- Public
- Created
- Mar 24, 2026
- Updated
- Mar 24, 2026
- SHA
- 880fa614
Model card
From the Hugging Face model README
PI0.5 ARX5 Multitask v1
PI0.5 fine-tuned on 186 ARX5 single-arm and bimanual manipulation datasets. Supports mixed 7D (single-arm) and 14D (bimanual) action spaces with per-dimension loss masking.
Model Details
- Base model: PI0.5 (pretrained
pi05_base) - Architecture:
Pi0Config(pi05=True, action_horizon=50) - Action space: 14D joint-space (bimanual). Single-arm datasets are zero-padded with
action_dim_maskto ignore unused dimensions during training. - Action type: Absolute (not delta)
- Training framework: openpi (JAX/Flax, Orbax checkpoints)
Training
- Datasets: 186 LeRobot v2.1 datasets (see
assets/training_mix_v1.jsonfor full list) - Prompts: Subtask descriptions extracted from dataset metadata
- Steps: 100,000 (training still in progress; checkpoints uploaded at milestones)
- Batch size: 36
- Learning rate: Cosine decay, 10k warmup, peak 5e-5
- Optimizer: AdamW, gradient clip norm 1.0
- EMA decay: 0.999
- Hardware: 4x GH200 GPU on Isambard AI cluster
Loss Progression
| Step | Train Loss | Grad Norm |
|---|---|---|
| 0 | 0.1667 | 1.665 |
| 5,000 | 0.0226 | 0.131 |
| 10,000 | 0.0219 | 0.108 |
| 15,000 | 0.0204 | 0.098 |
| 20,000 | 0.0187 | 0.086 |
| 25,000 | 0.0160 | 0.079 |
| 30,000 | 0.0160 | 0.081 |
| 35,000 | 0.0151 | 0.076 |
| 40,000 | 0.0140 | 0.074 |
| 45,000 | 0.0129 | 0.068 |
| 50,000 | 0.0126 | 0.069 |
| 55,000 | 0.0116 | 0.065 |
Full training curves: W&B dashboard
Available Checkpoints
| Checkpoint | Train Loss | SHA-256 (tar of params dir) |
|---|---|---|
checkpoints/25000/params | 0.016 | b856b219...c86f6218 |
checkpoints/40000/params | 0.014 | ad865b75...b81c9341 |
checkpoints/55000/params | 0.012 | 7ee69681...82fe5b2f |
Checkpoints contain model parameters only (Orbax format). Optimizer/training state is not included.
<details> <summary>Full SHA-256 hashes</summary>25000: b856b2198f0f04791b52257eb20a78072aa6612970a35f9864d45cefc86f6218
40000: ad865b75714a0d8d057074b45639cc18718dcdfed3b2ec2479cded32b81c9341
55000: 7ee69681991cdc5e04b4759d3bf93bca5dac6bc98639ec7b00202d2f82fe5b2f
Verify with: tar cf - -C checkpoints/<step> params | sha256sum
Assets
assets/norm_stats.json— 14-dim per-dimension quantile normalisation statisticsassets/training_mix_v1.json— list of all 186 dataset repo IDsassets/valid_indices.txt— ~1.4M filtered frame indices
Usage
Load a checkpoint in openpi:
from openpi.training import weight_loaders
loader = weight_loaders.CheckpointWeightLoader("path/to/checkpoints/55000/params")
Or reference it in a training config for further fine-tuning:
TrainConfig(
...
weight_loader=weight_loaders.CheckpointWeightLoader("path/to/checkpoints/55000/params"),
)
Citation
If you use this model, please cite openpi.
- Task
- Robotics
- License
- apache-2.0
- Created
- Mar 24, 2026
- Updated
- Mar 24, 2026