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diffusion_fold_towel

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1
+ Logs will be synced with wandb.
2
+ INFO 2025-11-17 15:27:26 ndb_utils.py:96 Track this run --> https://wandb.ai/jinprelude/lerobot/runs/qkpikcx2
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+ INFO 2025-11-17 15:27:26 ts/train.py:127 Creating dataset
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+ INFO 2025-11-17 15:27:27 ts/train.py:138 Creating policy
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+ INFO 2025-11-17 15:27:29 ts/train.py:144 Creating optimizer and scheduler
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+ INFO 2025-11-17 15:27:29 ts/train.py:156 Output dir: outputs/train/2025-11-17/15-27-24_diffusion
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+ INFO 2025-11-17 15:27:29 ts/train.py:159 cfg.steps=100000 (100K)
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+ INFO 2025-11-17 15:27:29 ts/train.py:160 dataset.num_frames=16910 (17K)
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+ INFO 2025-11-17 15:27:29 ts/train.py:161 dataset.num_episodes=50
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+ INFO 2025-11-17 15:27:29 ts/train.py:162 num_learnable_params=271145780 (271M)
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+ INFO 2025-11-17 15:27:29 ts/train.py:163 num_total_params=271145918 (271M)
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+ INFO 2025-11-17 15:27:29 ts/train.py:202 Start offline training on a fixed dataset
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+ INFO 2025-11-17 15:27:50 ts/train.py:232 step:200 smpl:2K ep:5 epch:0.09 loss:0.992 grdn:2.631 lr:2.0e-05 updt_s:0.072 data_s:0.032
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+ INFO 2025-11-17 15:28:09 ts/train.py:232 step:400 smpl:3K ep:9 epch:0.19 loss:0.389 grdn:3.058 lr:6.0e-05 updt_s:0.065 data_s:0.031
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+ INFO 2025-11-17 15:28:28 ts/train.py:232 step:600 smpl:5K ep:14 epch:0.28 loss:0.187 grdn:1.820 lr:9.5e-05 updt_s:0.065 data_s:0.031
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+ INFO 2025-11-17 15:28:47 ts/train.py:232 step:800 smpl:6K ep:19 epch:0.38 loss:0.126 grdn:1.293 lr:1.0e-04 updt_s:0.066 data_s:0.024
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416
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+ INFO 2025-11-17 17:58:46 ts/train.py:241 Checkpoint policy after step 100000
518
+ INFO 2025-11-17 17:59:01 ts/train.py:283 End of training
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