vla0-3b-ur5e-pick-3obj-90

VLA-0 (3B) - UR5e pick-3obj-90

Vision-Language-Action policy fine-tuned from Qwen/Qwen2.5-VL-3B-Instruct on jsiburian/ur5e-pick-3obj-90-lerobot-v2 (90 teleoperated UR5e demonstrations spanning 12 pick tasks across 3 distinct target objects) using the VLA-0 recipe.

Model details

Training

Loss is the standard VLA-0 next-token cross-entropy over discretized action tokens (vocab of 1000 bins per dim, 56 action tokens per sample). Random-guess baseline is ln(1000) ~ 6.91.

Files

dataset_stats.pkl is a Python pickle of {"out_ori_act": {"min": np.ndarray(7,), "max": np.ndarray(7,), "count": np.ndarray(1,)}}, which carries the per-dimension action min / max used to normalize / decode the 1000-bin action tokens.

Usage

Use the VLA-0 repo (or our RoboVerse-flavored fork) for inference; load weights directly with Qwen2_5_VLForConditionalGeneration.from_pretrained(...) and pair with dataset_stats.pkl for bin <-> continuous action conversion.

The dataset's task strings (12 of them) are short imperatives like "pick up the carrot", "pick up the cube", etc. Match the phrasing of your task prompt to one of those instructions when sampling actions.

Caveats

• Single-task imitation: no LR schedule, no test split, no held-out object. The model is fit hard to the 90-episode demonstration distribution.
• Two-camera tiled input - if you only have a third-person view at inference, results will be worse than what training metrics suggest.
• The companion PI-0.5 LoRA on the same dataset lives at jsiburian/pi05-ur5e-pick-3obj-90-lora.