metadata
license: apache-2.0
tags:
- vla-jepa
- robotics
- folding
- bimanual
- fine-tuned
- lerobot
base_model: lerobot/VLA-JEPA-Pretrain
datasets:
- lerobot/high_quality_folding
library_name: lerobot
VLA-JEPA Fine-tuned with Unfolding Robotics dataset
Model Description
This model is a VLA-JEPA policy fine-tuned for bimanual shirt folding on the OpenArm robot.
- Base model: lerobot/VLA-JEPA-Pretrain (VLA-JEPA pretrained on DROID)
- Fine-tuning dataset: lerobot/high_quality_folding
Training Details
Slurm Scripts and training config for job submission on LANTA are already provided in the repository.
- Cross-embodiment transfer: DROID (7D single-arm) → OpenArm (16D bimanual)
- Re-initialized layers: action_encoder, action_decoder, state_encoder
- Frozen backbone: Qwen3-VL-2B (inference only)
- Trainable params: 155M / 2.3B total
- Optimizer: AdamW, lr=3.75e-5, weight_decay=0.01
- Schedule: Cosine decay with warmup
- Batch size: 128
- Steps: 40000
- Precision: BF16
- RABC: Enabled (kappa=0.0265, SARM progress scores)
- Normalization: QUANTILES for state and action
- Training time: ~48-49 hours on 4x LANTA GPU Node (4xA100 40GB SXM)
Loss Curve
Usage
from lerobot.policies import make_policy
policy = make_policy(pretrained_name_or_path="chalkp/vla-jepa-folding")