Final checkpoint at 30000 / 30000 training steps. OpenVLA-OFT checkpoint fine-tuned on real-world XArm data, setting 2: cup stacking. LoRA weights (rank 32) are already merged into the openvla/openvla-7b base — this repo is a standalone model.

Training

Recipe: OpenVLA-OFT (L1 regression, FiLM, parallel decoding)
Steps: 30000 (LR 5e-4, decay after 20000), effective batch size 32
Action chunk: 25, action dim: 7, proprio dim: 6 (BOUNDS_Q99 normalization)
Inputs: 2 images (3rd-person + wrist) + proprio

Merged model shards (model-*.safetensors)
action_head--30000_checkpoint.pt, proprio_projector--30000_checkpoint.pt, vision_backbone--30000_checkpoint.pt — OFT components needed for deployment
dataset_statistics.json — action/proprio normalization statistics
oft_training_config.json — training configuration snapshot

Use with the openvla-oft codebase; set XArm constants (chunk 25, action dim 7, proprio dim 6) in prismatic/vla/constants.py before deployment.

AAyano/oft_setting2_chunksize25_batch32_30k

oft_setting2_chunksize25_batch32_30k

Training

Contents

AAyano/
oft_setting2_chunksize25_batch32_30k