DART-PT-Flow-LIBERO

ODEWorld image-goal PT-Flow checkpoint retrained with DART (Decode-Augmented Rollout Training) on official LIBERO demonstrations, starting from ldxxx/ODEWorld-PT-Flow-LIBERO. DART keeps the representation frozen (DINOv2 encoder, RAE decoder, goal predictor) and retrains only the flow with decode-path supervision, repairing the motion collapse of the pretrained and latent-only-retrained flows. The checkpoint includes the frozen DINOv2 backbone.

Training configuration

• Window 5, decode-path supervision weight 0.5, DINOv2 region loss weight 0.5, 4 clusters
• 26,000 steps, batch size 2, learning rate 1e-5, seed 20260910
• Selected by lowest validation loss over the task-balanced 130-task Scale-M split

Results (offline open-loop, 64-step image-goal rollouts at 256×256)

Protocol	Metric	Latent-only parent	DART (this checkpoint)
100-demo full protocol	mean / median L1 ↓	10.311 / 9.64	9.944 / 9.30
hard-5 subset	motion concentration conc3 ↓ (GT 0.099)	0.236–0.447	0.145
hard-5 subset	total motion ↑	21–58	69
650-demo split	PSNR ↑ / LPIPS ↓	19.24–19.45 / 0.196–0.212	19.67–19.77 / 0.190–0.195

Usage

Load with the release code:

from models import Dinov2PTflowImgoal

flow = Dinov2PTflowImgoal.from_pretrained("pzr114514/DART-PT-Flow-LIBERO")

Training, evaluation, and inference scripts: github.com/Mangkhut160/icassp2027

Scope

This checkpoint is trained and evaluated purely as an offline open-loop predictor on LIBERO demonstration frames. It is not a robot policy: no LIBERO/MuJoCo environment is started, no actions are executed, and no task success rate is measured.