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awrenn53
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groot-n17-so101-cleanup-vials-relact-bs128-lr1e4-albu-h16-015000

Robotics
LeRobot
Safetensors
imitation-learning
groot
so101
gr00t-n1.7
albumentations
Model card Files
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  1. README.md +41 -0
  2. config.json +100 -0
  3. model.safetensors +3 -0
  4. policy_postprocessor.json +988 -0
  5. policy_preprocessor.json +1084 -0
  6. policy_preprocessor_step_2_groot_n1_7_pack_inputs_v1.safetensors +3 -0
  7. train_config.json +245 -0
README.md ADDED
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+ ---
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+ library_name: lerobot
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+ base_model: nvidia/GR00T-N1.7-3B
4
+ datasets:
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+ - sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121
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+ tags:
7
+ - lerobot
8
+ - robotics
9
+ - imitation-learning
10
+ - groot
11
+ - so101
12
+ - gr00t-n1.7
13
+ - albumentations
14
+ ---
15
+
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+ # GR00T N1.7 SO-101 Cleanup Vials, Albumentations, H16, 15k
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+
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+ Fine-tuned GR00T N1.7 policy for the SO-101 cleanup-vials-into-rack task.
19
+
20
+ ## Checkpoint
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+
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+ - Source run: `groot-n17-so101-cleanup-vials-relact-bs128-lr1e4-preset-albu-20k-save5k-20260630-1845`
23
+ - Checkpoint step: `015000`
24
+ - Base model: `nvidia/GR00T-N1.7-3B`
25
+ - Dataset: `sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121`
26
+ - Embodiment tag: `new_embodiment`
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+ - Batch size: `128`
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+ - Chunk size: `16`
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+ - Action steps: `16`
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+ - Optimizer: AdamW, learning rate `1e-4`, weight decay `1e-5`
31
+ - Scheduler: cosine decay with warmup, `500` warmup steps
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+ - Relative actions: enabled, excluding `gripper`
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+ - GR00T preprocessor `use_albumentations`: `true`
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+
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+ ## Usage
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+
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+ Load this policy with LeRobot using the uploaded Hub repo ID:
38
+
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+ ```bash
40
+ uv run lerobot-eval --policy.path=awrenn53/groot-n17-so101-cleanup-vials-relact-bs128-lr1e4-albu-h16-015000
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+ ```
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+ {
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+ "report_to": "wandb",
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+ }
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