oft_setting1_chunksize25_batch32_20k

OpenVLA-OFT checkpoint fine-tuned on real-world XArm data, setting 1: pick cube and place into plastic cup. Intermediate checkpoint at 20000 / 30000 training steps (see oft_setting1_chunksize25_batch32 for the final 30k-step model). 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: 20000 of 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

Contents

• Merged model shards (model-*.safetensors)
• action_head--20000_checkpoint.pt, proprio_projector--20000_checkpoint.pt, vision_backbone--20000_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.