fine-tuning-openvla-oft

Fine-tuning OpenVLA-OFT for robot action generation involves using LoRA adaptation and optional FiLM conditioning on your dataset. You can apply this workflow to LIBERO and ALOHA pipelines to train and evaluate models reproducibly.

FiLM conditioning in OpenVLA-OFT provides improved grounding for robot action generation by modulating features. You should use it when adapting LoRA-based models for tasks requiring enhanced visual or proprioceptive context alignment.

Yes, you need PyTorch, Transformers, PEFT, and the OpenVLA-OFT codebase to run these fine-tuning workflows. These dependencies are required for LoRA adaptation, FiLM conditioning, and model evaluation.

Yes, OpenVLA-OFT fine-tuning supports both LIBERO-like simulation tasks and ALOHA real-world pipelines. It handles multi-image inputs and proprioception for reproducible training and evaluation across server-client setups.

Merging LoRA adapters after fine-tuning OpenVLA-OFT involves combining the adapted weights with the base model. This step is crucial for reproducible deployment via the VLA server for client evaluations.

Matching action heads in OpenVLA-OFT is necessary to ensure the model outputs align with your specific robot's action space. Mismatches can cause evaluation failures or invalid action generation during deployment.