fine-tuning-openvla-oft

# OpenVLA-OFT Fine-tuning and evaluation workflows for OpenVLA-OFT and OpenVLA-OFT+ from the official `openvla-oft` codebase. Covers blank-machine setup plus LoRA-based adaptation of OpenVLA for robot action generation with continuous action prediction heads. ## Quick start Clone the public repo, follow the official setup, then evaluate a pretrained LIBERO checkpoint: ```bash git clone https://github.com/moojink/openvla-oft.git cd openvla-oft python experiments/robot/libero/run_libero_eval.py \ --pretrained_checkpoint moojink/openvla-7b-oft-finetuned-libero-spatial \ --task_suite_name libero_spatial \ --center_crop True \ --num_trials_per_task 50 \ --seed 7 ``` ## Core concepts **What OpenVLA-OFT changes**: Standard OpenVLA tokenizes continuous actions into discrete bins, losing precision. OFT replaces this with dedicated continuous action heads (L1 regression or diffusion) while keeping the VLA backbone frozen and adapting via LoRA. **OFT vs OFT+ variants**: | Variant | FiLM | Images | Typical use | |---------|------|--------|-------------| | OFT | Off | 2 (front + wrist) | LIBERO simulation | | OFT+ | On | 3 (high + left + right wrist) | ALOHA real-world | **Key architecture choices**: - **LoRA adaptation**: Rank-32 LoRA on VLA backbone (no full fine-tuning needed) - **Continuous actions**: L1 regression head (default) or diffusion head - **FiLM conditioning**: Feature-wise Linear Modulation for stronger language grounding in OFT+ - **Multi-image input**:...