LoRAFleet/hifvla-libero-lora

HiF-VLA — LIBERO per-suite LoRA experts

Per-suite OpenVLA-OFT LoRA adapters (plus action_head / motion_* heads) extracted from the published HiF-VLA checkpoints. The OpenVLA-7B base weights are not included.

Suite	Source repo	LoRA r/alpha	closed-loop success (measured)
spatial	minnielin/hifvla-libero-spatial	32 / 16	100% (10 ep)
object	minnielin/hifvla-libero-object	32 / 16	96% (50 ep)
goal	minnielin/hifvla-libero-goal	32 / 16	100% (50 ep)
long	minnielin/hifvla-libero-long	32 / 16	96% (50 ep)

• Base model: openvla/openvla-7b
• Method: HiF-VLA (Hindsight/Insight/Foresight motion representation), CVPR 2026, arXiv 2512.09928
• Upstream code: https://github.com/minnie-lin/HiF-VLA

Each <suite>/ contains lora_adapter/ (adapter_config.json + adapter_model.safetensors), action_head--checkpoint.pt, motion_encoder--checkpoint.pt, motion_manager--checkpoint.pt, proprio_projector--checkpoint.pt, config/tokenizer files.

Reproduce the evaluation

Needs the HiF-VLA repo + LIBERO + a moojink OpenVLA-OFT / transformers-fork environment, plus ffmpeg (the eval uses it for motion-vector extraction; imageio-ffmpeg's bundled binary works) and av. mvextractor is training-only; guard its import.

python experiments/robot/libero/run_libero_eval.py \
  --use_proprio True --num_images_in_input 2 --use_film False \
  --pretrained_checkpoint <suite checkpoints merged with base> \
  --task_suite_name libero_<suite> --history_length 8 --unnorm_key libero_<suite>_no_noops