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:

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: Configurable 2 or 3 camera streams via num_images_in_input

Compute requirements

Task	GPU	VRAM	Notes
LIBERO evaluation	1x A100/A40	~16 GB	Single GPU
ALOHA evaluation	1x A100/A40	~18 GB	Single GPU
LIBERO fine-tuning	8x A100	~27 GB/GPU	Paper default
ALOHA fine-tuning (OFT+)	8x A100	~35 GB/GPU	FiLM + 3 images
LoRA merge	1x any GPU	~16 GB	One-time step

Expected performance benchmarks