FastWAM UR3 fine-tune (drawer + blue_basket + stacking_cubes, 3-task joint)

Fast-WAM (uncond) fine-tuned on EmbodyX/UR3 real-world tasks drawer + blue_basket + stacking_cubes (trained jointly on all three), initialized weights-only from the RoboCOIN-pretrained Fast-WAM. 4000 steps, lr 1e-4 cosine, AdamW(0.9,0.95), bf16, 3 cams (top + L/R wrist, 240x320), 65-frame clips, action_video_freq_ratio 8, action&state dim 14.

Tasks:

  • drawer: "open the drawer, put the white box inside the drawer then close the drawer"
  • blue_basket: "put the medicine then the measuring tape inside the blue basket"
  • stacking_cubes: "put the green cube on top of the black cube and put the red cube on top of the green cube"

NOTE: only ~300 real episodes (100/task) so the model overfits fast (loss_action ~0.066 by step 4000) -> pick the best checkpoint by REAL-ROBOT success rate, not the last step (2500/3000/3500 often generalize better than 4000). Files: ur3_3task_step{2500,3000,3500,4000}.pt (weights) + ur3_3task_dataset_stats.json (norm stats, required for inference).

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