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SleepMastger/
fruit-picking-fastwam

Name: fruit-picking-fastwam
Availability: InStock

pytorch

robotics

video-generation

vision-language-action

fruit-picking

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Fruit-picking Fastwam

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: FastWAM full 30-layer video + 30-layer action MoT.

Checkpoints follow the same layout as the existing WAM repositories:

checkpoints/weights/step_003150.pt — SHA-256 5fcd3ccfa8d6307a24dedfc9f8aadbec69e698a5a4218ad94f275914474dc9f9
checkpoints/weights/step_006300.pt — SHA-256 eb3dca41eec86722bcfe5776bb46738417373d17cc11da1e65f4c5b4be34b0ab
checkpoints/weights/step_009450.pt — SHA-256 f6c54e322ee97171177d68c671254faa1e31045d803f0943e60549a1fd5e13ff
checkpoints/weights/step_012600.pt — SHA-256 9fc1147a649aeff4139d00fe70adc07f52ff46e7ffc6dc1b379e29d0aa18ed85

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.

SleepMastger/fruit-picking-fastwam