SleepMastger/fruit-picking-flashwam

Fruit-picking Flashwam

Public archival bundle for the fruit-picking model trained by Amin.

Status

This is a partial checkpoint: step 12,600 of 18,900 (epoch 20 of 30). The job terminated when its shared /dev/shm video staging disappeared; it did not finish the planned 30 epochs. The model has not been evaluated on a robot, and the physical meaning of the -1/+1 gripper polarity still needs confirmation.

Architecture: FlashWAM M1 fused-KV, fixed-RoPE, 1-layer action expert.

Checkpoints follow the same layout as the existing WAM repositories:

• checkpoints/weights/step_003150.pt — SHA-256 2dc3d4ce58a592944975fc6aa2214f3f22cf724b08fa3815588d4d83319e31b0
• checkpoints/weights/step_006300.pt — SHA-256 660268a99a6e298ad5e3948f4784e7d8be6b4e0ad0517d9404b52c1dc7797145
• checkpoints/weights/step_009450.pt — SHA-256 dc0497a96a8c2c7bcf072d00e700302707f260bf6be0c7fa77567520f101ca01
• checkpoints/weights/step_012600.pt — SHA-256 96c9587141286f2bef8f7c38bc31816e5f9ce689f3c66500b5fdf1d6f3b83a06

Conditioning

Exact task text:

Lift the lid, put it aside, and pick the black plum.

conditioning/text_embedding.pt is the exact cached T5 embedding consumed during training. It was generated with the Wan text stack, context length 128, using Wan-AI/Wan2.1-T2V-1.3B as the tokenizer model reference. The resolved training config sets load_text_encoder: false, so this cached tensor is part of the required inference bundle.

Input processing and normalization

• Two 256x256 RGB cameras (agentview, then wrist).
• Each camera is converted to a tensor and resized to 224x224.
• Cameras are concatenated horizontally to 224x448.
• Horizon: 33 observations; 32 action transitions at 10 Hz.
• Original 15-D state was converted to 8-D: eef_xyz(3) + quat-to-axis-angle(3) + [gripper_width/2, -gripper_width/2].
• Action is 7-D: delta XYZ, delta rotation XYZ, and gripper.
• Delta/padding mask is [true, true, true, true, true, true, false]; the gripper channel is absolute rather than delta.
• dataset_stats.json contains the exact min/max normalization statistics used by this run.

Attention masks

The resolved model uses:

• video_attention_mask_mode: first_frame_causal: first-frame queries cannot attend to later video frames; later-frame queries can attend to all video tokens.
• action_group_causal_mask_mode: group_diagonal: each video temporal group attends only to the corresponding action-token group.
• Text cross-attention is enabled for the action expert.

The exact implementations and preprocessing classes are included under training_code/; the resolved config is config.yaml. No license is asserted here for the bundled upstream code; its original terms continue to apply.

Base components

This weights-only checkpoint is not standalone. It references Wan-AI/Wan2.2-TI2V-5B and requires the matching Wan VAE plus the included FastWAM code/configuration. PyTorch .pt files may contain pickled objects; load only in a trusted environment.