diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/README.md b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d4f4749b3e1d6548b0e53818330c661bd78e19c
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+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/README.md
@@ -0,0 +1,12 @@
+# Real150 ViTacDreamer Encoder Policy Artifacts (2026-07-27)
+
+Tasks: insert_tube, wipe_board.
+
+Includes final ACT policy checkpoints trained with frozen ViTacDreamer Stage2 encoder features, per-task dataset_stats.pkl, training plots, logs, config, and the ViTacDreamer Stage2 encoder checkpoint used for feature precompute/deployment.
+
+Policy config: train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml
+Feature cache used during training: vitacdreamer_features_real150_stage2_depthdelta_20260727
+
+Best validation:
+- insert_tube: val loss 0.059148 @ epoch 11
+- wipe_board: val loss 0.068180 @ epoch 8
diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/dataset_stats.pkl b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/dataset_stats.pkl
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_best.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_best.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_last.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_last.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_step16000.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/policy_step16000.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_kl_seed_0.png b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_kl_seed_0.png
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_l1_seed_0.png b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_l1_seed_0.png
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_loss_seed_0.png b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-insert_tube/train_val_loss_seed_0.png
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/dataset_stats.pkl b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/dataset_stats.pkl
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_best.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_best.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_last.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_last.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_step16000.ckpt b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/policy_step16000.ckpt
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/train_val_kl_seed_0.png b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/train_val_kl_seed_0.png
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/train_val_loss_seed_0.png b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/act-wipe_board/train_val_loss_seed_0.png
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/encoder/stage2_v2_encoder_only.pth b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/encoder/stage2_v2_encoder_only.pth
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diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_insert_tube_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu2.log b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_insert_tube_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu2.log
new file mode 100644
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+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_insert_tube_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu2.log
@@ -0,0 +1,49 @@
+/dev/shm/muse/envs/vitacdreamer_prior512/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/dev/shm/muse/envs/vitacdreamer_prior512/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+
+Data from: ./data/sim-insert_tube/real-150
+
+build_ACT_model_and_optimizer Namespace(lr=1e-05, lr_backbone=1e-05, batch_size=32, weight_decay=0.0001, epochs=300, lr_drop=200, clip_max_norm=0.1, dilation=False, pre_norm=False, vitacdreamer_cross_attn_layers='none', masks=False, eval=False, onscreen_render=False, num_epochs=1, state_dim=8, kl_weight=7.5, chunk_size=15, hidden_dim=512, dim_feedforward=3200, temporal_agg=True, device='cuda:0', ckpt_dir='./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k', policy_class='ACT', num_steps=16000, num_workers=4, save_freq=1000, position_embedding='sine', lr_vision_backbone=1e-05, vitacdreamer_adapter_lr=5e-05, backbone='resnet18', nheads=8, enc_layers=4, dec_layers=7, dropout=0.025, camera_names=['cam_high'], tactile_names=['tac_left', 'tac_right'], lr_tactile_backbone=1e-05, tactile_masks=False, tactile_backbone='resnet18', tactile_ckpt=None, tactile_dilation=False, use_vitacdreamer_feature=True, vitacdreamer_history_len=5, vitacdreamer_sample_stride=5, vitacdreamer_feature_dim=512, vitacdreamer_fusion_mode='feature_query_policy_kv', finetune_vitacdreamer_encoder=False, vitacdreamer_checkpoint='/dev/shm/muse/checkpoints/real_insert_tube_wipe_board_150_h32_20260725/stage2_prior512_hlen5_depthdelta_120train30val_b192_from_stage1_prior512_hlen5_120train30val_b192/stage2_v2_encoder_only.pth', vitacdreamer_feature_cache_dir='./data/sim-{task_stem}/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727', vitacdreamer_task_order=['insert_tube', 'wipe_board'], task_name='sim-insert_tube-real-150', config_path='./train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml', seed=0, distributed=False, rank=0, local_rank=0)
+Namespace(lr=1e-05, lr_backbone=1e-05, batch_size=32, weight_decay=0.0001, epochs=300, lr_drop=200, clip_max_norm=0.1, dilation=False, pre_norm=False, vitacdreamer_cross_attn_layers='none', masks=False, eval=False, onscreen_render=False, num_epochs=1, state_dim=8, kl_weight=7.5, chunk_size=15, hidden_dim=512, dim_feedforward=3200, temporal_agg=True, device='cuda:0', ckpt_dir='./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k', policy_class='ACT', num_steps=16000, num_workers=4, save_freq=1000, position_embedding='sine', lr_vision_backbone=1e-05, vitacdreamer_adapter_lr=5e-05, backbone='resnet18', nheads=8, enc_layers=4, dec_layers=7, dropout=0.025, camera_names=['cam_high'], tactile_names=['tac_left', 'tac_right'], lr_tactile_backbone=1e-05, tactile_masks=False, tactile_backbone='resnet18', tactile_ckpt=None, tactile_dilation=False, use_vitacdreamer_feature=True, vitacdreamer_history_len=5, vitacdreamer_sample_stride=5, vitacdreamer_feature_dim=512, vitacdreamer_fusion_mode='feature_query_policy_kv', finetune_vitacdreamer_encoder=False, vitacdreamer_checkpoint='/dev/shm/muse/checkpoints/real_insert_tube_wipe_board_150_h32_20260725/stage2_prior512_hlen5_depthdelta_120train30val_b192_from_stage1_prior512_hlen5_120train30val_b192/stage2_v2_encoder_only.pth', vitacdreamer_feature_cache_dir='./data/sim-{task_stem}/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727', vitacdreamer_task_order=['insert_tube', 'wipe_board'], task_name='sim-insert_tube-real-150', config_path='./train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml', seed=0, distributed=False, rank=0, local_rank=0)
+number of parameters: 100.95M
+KL Weight 7.5
+
  0%|          | 0/16000 [00:00<?, ?it/s]/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+
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  8%|▊         | 1256/16000 [02:00<19:41, 12.48it/s, epoch=0, loss=0.44] Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+
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 12%|█▏        | 1963/16000 [03:32<28:04,  8.33it/s, epoch=1, loss=0.346]
 12%|█▏        | 1964/16000 [03:32<25:49,  9.06it/s, epoch=1, loss=0.346]
 12%|█▏        | 1964/16000 [03:32<25:49,  9.06it/s, epoch=1, loss=0.374]
 12%|█▏        | 1965/16000 [03:32<25:49,  9.06it/s, epoch=1, loss=0.33] 
 12%|█▏        | 1966/16000 [03:32<24:45,  9.44it/s, epoch=1, loss=0.33]
 12%|█▏        | 1966/16000 [03:33<24:45,  9.44it/s, epoch=1, loss=0.379]
 12%|█▏        | 1967/16000 [03:33<25:39,  9.12it/s, epoch=1, loss=0.379]
 12%|█▏        | 1967/16000 [03:33<25:39,  9.12it/s, epoch=1, loss=0.36] 
 12%|█▏        | 1968/16000 [03:33<25:39,  9.12it/s, epoch=1, loss=0.379]
 12%|█▏        | 1969/16000 [03:33<24:06,  9.70it/s, epoch=1, loss=0.379]
 12%|█▏        | 1969/16000 [03:33<24:06,  9.70it/s, epoch=1, loss=0.402]
 12%|█▏        | 1970/16000 [03:33<24:45,  9.44it/s, epoch=1, loss=0.402]
 12%|█▏        | 1970/16000 [03:33<24:45,  9.44it/s, epoch=1, loss=0.356]
 12%|█▏        | 1971/16000 [03:33<24:45,  9.44it/s, epoch=1, loss=0.372]
 12%|█▏        | 1972/16000 [03:33<23:32,  9.93it/s, epoch=1, loss=0.372]
 12%|█▏        | 1972/16000 [03:33<23:32,  9.93it/s, epoch=1, loss=0.357]
 12%|█▏        | 1973/16000 [03:33<23:32,  9.93it/s, epoch=1, loss=0.351]
 12%|█▏        | 1974/16000 [03:33<23:15, 10.05it/s, epoch=1, loss=0.351]
 12%|█▏        | 1974/16000 [03:33<23:15, 10.05it/s, epoch=1, loss=0.381]
 12%|█▏        | 1975/16000 [03:33<24:01,  9.73it/s, epoch=1, loss=0.381]
 12%|█▏        | 1975/16000 [03:33<24:01,  9.73it/s, epoch=1, loss=0.392]
 12%|█▏        | 1976/16000 [03:34<24:01,  9.73it/s, epoch=1, loss=0.36] 
 12%|█▏        | 1977/16000 [03:34<22:43, 10.28it/s, epoch=1, loss=0.36]
 12%|█▏        | 1977/16000 [03:34<22:43, 10.28it/s, epoch=1, loss=0.371]
 12%|█▏        | 1978/16000 [03:34<22:43, 10.28it/s, epoch=1, loss=0.376]
 12%|█▏        | 1979/16000 [03:34<21:46, 10.73it/s, epoch=1, loss=0.376]
 12%|█▏        | 1979/16000 [03:34<21:46, 10.73it/s, epoch=1, loss=0.359]
 12%|█▏        | 1980/16000 [03:34<21:46, 10.73it/s, epoch=1, loss=0.356]
 12%|█▏        | 1981/16000 [03:34<21:17, 10.97it/s, epoch=1, loss=0.356]
 12%|█▏        | 1981/16000 [03:34<21:17, 10.97it/s, epoch=1, loss=0.377]
 12%|█▏        | 1982/16000 [03:34<21:17, 10.97it/s, epoch=1, loss=0.36] 
 12%|█▏        | 1983/16000 [03:34<22:52, 10.21it/s, epoch=1, loss=0.36]
 12%|█▏        | 1983/16000 [03:34<22:52, 10.21it/s, epoch=1, loss=0.379]
 12%|█▏        | 1984/16000 [03:34<22:52, 10.21it/s, epoch=1, loss=0.353]
 12%|█▏        | 1985/16000 [03:34<23:44,  9.84it/s, epoch=1, loss=0.353]
 12%|█▏        | 1985/16000 [03:34<23:44,  9.84it/s, epoch=1, loss=0.371]
 12%|█▏        | 1986/16000 [03:35<23:44,  9.84it/s, epoch=1, loss=0.36] 
 12%|█▏        | 1987/16000 [03:35<22:47, 10.25it/s, epoch=1, loss=0.36]
 12%|█▏        | 1987/16000 [03:35<22:47, 10.25it/s, epoch=1, loss=0.359]
 12%|█▏        | 1988/16000 [03:35<22:47, 10.25it/s, epoch=1, loss=0.32] 
 12%|█▏        | 1989/16000 [03:35<25:52,  9.02it/s, epoch=1, loss=0.32]
 12%|█▏        | 1989/16000 [03:35<25:52,  9.02it/s, epoch=1, loss=0.372]
 12%|█▏        | 1990/16000 [03:35<26:31,  8.80it/s, epoch=1, loss=0.372]
 12%|█▏        | 1990/16000 [03:35<26:31,  8.80it/s, epoch=1, loss=0.358]
 12%|█▏        | 1991/16000 [03:35<26:31,  8.80it/s, epoch=1, loss=0.35] 
 12%|█▏        | 1992/16000 [03:35<24:10,  9.66it/s, epoch=1, loss=0.35]
 12%|█▏        | 1992/16000 [03:35<24:10,  9.66it/s, epoch=1, loss=0.355]
 12%|█▏        | 1993/16000 [03:35<24:04,  9.70it/s, epoch=1, loss=0.355]
 12%|█▏        | 1993/16000 [03:35<24:04,  9.70it/s, epoch=1, loss=0.396]
 12%|█▏        | 1994/16000 [03:35<24:38,  9.47it/s, epoch=1, loss=0.396]
 12%|█▏        | 1994/16000 [03:35<24:38,  9.47it/s, epoch=1, loss=0.385]
 12%|█▏        | 1995/16000 [03:35<25:29,  9.16it/s, epoch=1, loss=0.385]
 12%|█▏        | 1995/16000 [03:36<25:29,  9.16it/s, epoch=1, loss=0.35] 
 12%|█▏        | 1996/16000 [03:36<25:29,  9.16it/s, epoch=1, loss=0.37]
 12%|█▏        | 1997/16000 [03:36<25:57,  8.99it/s, epoch=1, loss=0.37]
 12%|█▏        | 1997/16000 [03:36<25:57,  8.99it/s, epoch=1, loss=0.354]
 12%|█▏        | 1998/16000 [03:36<26:58,  8.65it/s, epoch=1, loss=0.354]
 12%|█▏        | 1998/16000 [03:36<26:58,  8.65it/s, epoch=1, loss=0.349]
 12%|█▏        | 1999/16000 [03:36<26:58,  8.65it/s, epoch=1, loss=0.355]
 12%|█▎        | 2000/16000 [03:36<24:13,  9.63it/s, epoch=1, loss=0.355]
 12%|█▎        | 2000/16000 [03:37<24:13,  9.63it/s, epoch=1, loss=0.359]
 13%|█▎        | 2001/16000 [03:37<1:04:30,  3.62it/s, epoch=1, loss=0.359]
 13%|█▎        | 2001/16000 [03:37<1:04:30,  3.62it/s, epoch=1, loss=0.356]
 13%|█▎        | 2002/16000 [03:37<1:04:30,  3.62it/s, epoch=1, loss=0.359]
 13%|█▎        | 2003/16000 [03:37<46:38,  5.00it/s, epoch=1, loss=0.359]  
 13%|█▎        | 2003/16000 [03:37<46:38,  5.00it/s, epoch=1, loss=0.377]
 13%|█▎        | 2004/16000 [03:37<46:38,  5.00it/s, epoch=1, loss=0.358]
 13%|█▎        | 2005/16000 [03:37<36:53,  6.32it/s, epoch=1, loss=0.358]
 13%|█▎        | 2005/16000 [03:37<36:53,  6.32it/s, epoch=1, loss=0.359]
 13%|█▎        | 2006/16000 [03:37<36:53,  6.32it/s, epoch=1, loss=0.383]
 13%|█▎        | 2007/16000 [03:37<30:58,  7.53it/s, epoch=1, loss=0.383]
 13%|█▎        | 2007/16000 [03:37<30:58,  7.53it/s, epoch=1, loss=0.38] 
 13%|█▎        | 2008/16000 [03:38<30:58,  7.53it/s, epoch=1, loss=0.371]
 13%|█▎        | 2009/16000 [03:38<29:18,  7.96it/s, epoch=1, loss=0.371]
 13%|█▎        | 2009/16000 [03:38<29:18,  7.96it/s, epoch=1, loss=0.343]
 13%|█▎        | 2010/16000 [03:38<29:18,  7.96it/s, epoch=1, loss=0.357]
 13%|█▎        | 2011/16000 [03:38<27:51,  8.37it/s, epoch=1, loss=0.357]
 13%|█▎        | 2011/16000 [03:38<27:51,  8.37it/s, epoch=1, loss=0.354]
 13%|█▎        | 2012/16000 [03:38<27:54,  8.35it/s, epoch=1, loss=0.354]
 13%|█▎        | 2012/16000 [03:38<27:54,  8.35it/s, epoch=1, loss=0.383]
 13%|█▎        | 2013/16000 [03:38<27:51,  8.37it/s, epoch=1, loss=0.383]
 13%|█▎        | 2013/16000 [03:38<27:51,  8.37it/s, epoch=1, loss=0.361]
 13%|█▎        | 2014/16000 [03:38<31:00,  7.52it/s, epoch=1, loss=0.361]
 13%|█▎        | 2014/16000 [03:38<31:00,  7.52it/s, epoch=1, loss=0.367]
 13%|█▎        | 2015/16000 [03:38<32:43,  7.12it/s, epoch=1, loss=0.367]
 13%|█▎        | 2015/16000 [03:39<32:43,  7.12it/s, epoch=1, loss=0.361]
 13%|█▎        | 2016/16000 [03:39<31:49,  7.32it/s, epoch=1, loss=0.361]
 13%|█▎        | 2016/16000 [03:39<31:49,  7.32it/s, epoch=1, loss=0.371]
 13%|█▎        | 2017/16000 [03:39<31:49,  7.32it/s, epoch=1, loss=0.366]
 13%|█▎        | 2018/16000 [03:39<27:37,  8.43it/s, epoch=1, loss=0.366]
 13%|█▎        | 2018/16000 [03:39<27:37,  8.43it/s, epoch=1, loss=0.384]
 13%|█▎        | 2019/16000 [03:39<26:54,  8.66it/s, epoch=1, loss=0.384]
 13%|█▎        | 2019/16000 [03:39<26:54,  8.66it/s, epoch=1, loss=0.335]
 13%|█▎        | 2020/16000 [03:39<26:54,  8.66it/s, epoch=1, loss=0.34] 
 13%|█▎        | 2021/16000 [03:39<24:12,  9.63it/s, epoch=1, loss=0.34]
 13%|█▎        | 2021/16000 [03:39<24:12,  9.63it/s, epoch=1, loss=0.363]
 13%|█▎        | 2022/16000 [03:39<25:39,  9.08it/s, epoch=1, loss=0.363]
 13%|█▎        | 2022/16000 [03:39<25:39,  9.08it/s, epoch=1, loss=0.37] 
 13%|█▎        | 2023/16000 [03:39<26:51,  8.67it/s, epoch=1, loss=0.37]
 13%|█▎        | 2023/16000 [03:39<26:51,  8.67it/s, epoch=1, loss=0.342]
 13%|█▎        | 2024/16000 [03:39<26:51,  8.67it/s, epoch=1, loss=0.338]
 13%|█▎        | 2025/16000 [03:39<25:19,  9.19it/s, epoch=1, loss=0.338]
 13%|█▎        | 2025/16000 [03:40<25:19,  9.19it/s, epoch=1, loss=0.366]
 13%|█▎        | 2026/16000 [03:40<25:09,  9.26it/s, epoch=1, loss=0.366]
 13%|█▎        | 2026/16000 [03:40<25:09,  9.26it/s, epoch=1, loss=0.357]
 13%|█▎        | 2027/16000 [03:40<25:09,  9.26it/s, epoch=1, loss=0.359]
 13%|█▎        | 2028/16000 [03:40<22:24, 10.39it/s, epoch=1, loss=0.359]
 13%|█▎        | 2028/16000 [03:40<22:24, 10.39it/s, epoch=1, loss=0.349]
 13%|█▎        | 2029/16000 [03:40<22:24, 10.39it/s, epoch=1, loss=0.37] 
 13%|█▎        | 2030/16000 [03:40<21:14, 10.96it/s, epoch=1, loss=0.37]
 13%|█▎        | 2030/16000 [03:40<21:14, 10.96it/s, epoch=1, loss=0.355]
 13%|█▎        | 2031/16000 [03:40<21:14, 10.96it/s, epoch=1, loss=0.365]
 13%|█▎        | 2032/16000 [03:40<20:44, 11.23it/s, epoch=1, loss=0.365]
 13%|█▎        | 2032/16000 [03:40<20:44, 11.23it/s, epoch=1, loss=0.362]
 13%|█▎        | 2033/16000 [03:40<20:44, 11.23it/s, epoch=1, loss=0.34] 
 13%|█▎        | 2034/16000 [03:40<20:32, 11.33it/s, epoch=1, loss=0.34]
 13%|█▎        | 2034/16000 [03:40<20:32, 11.33it/s, epoch=1, loss=0.38]
 13%|█▎        | 2035/16000 [03:40<20:32, 11.33it/s, epoch=1, loss=0.398]
 13%|█▎        | 2036/16000 [03:40<20:04, 11.60it/s, epoch=1, loss=0.398]
 13%|█▎        | 2036/16000 [03:40<20:04, 11.60it/s, epoch=1, loss=0.371]
 13%|█▎        | 2037/16000 [03:41<20:04, 11.60it/s, epoch=1, loss=0.352]
 13%|█▎        | 2038/16000 [03:41<19:36, 11.87it/s, epoch=1, loss=0.352]
 13%|█▎        | 2038/16000 [03:41<19:36, 11.87it/s, epoch=1, loss=0.365]
 13%|█▎        | 2039/16000 [03:41<19:36, 11.87it/s, epoch=1, loss=0.36] 
 13%|█▎        | 2040/16000 [03:41<19:38, 11.84it/s, epoch=1, loss=0.36]
 13%|█▎        | 2040/16000 [03:41<19:38, 11.84it/s, epoch=1, loss=0.347]
 13%|█▎        | 2041/16000 [03:41<19:38, 11.84it/s, epoch=1, loss=0.375]
 13%|█▎        | 2042/16000 [03:41<19:45, 11.77it/s, epoch=1, loss=0.375]
 13%|█▎        | 2042/16000 [03:41<19:45, 11.77it/s, epoch=1, loss=0.363]
 13%|█▎        | 2043/16000 [03:41<19:45, 11.77it/s, epoch=1, loss=0.347]
 13%|█▎        | 2044/16000 [03:41<21:05, 11.03it/s, epoch=1, loss=0.347]
 13%|█▎        | 2044/16000 [03:41<21:05, 11.03it/s, epoch=1, loss=0.401]
 13%|█▎        | 2045/16000 [03:41<21:05, 11.03it/s, epoch=1, loss=0.354]
 13%|█▎        | 2046/16000 [03:41<21:40, 10.73it/s, epoch=1, loss=0.354]
 13%|█▎        | 2046/16000 [03:41<21:40, 10.73it/s, epoch=1, loss=0.364]
 13%|█▎        | 2047/16000 [03:41<21:40, 10.73it/s, epoch=1, loss=0.393]
 13%|█▎        | 2048/16000 [03:41<21:21, 10.89it/s, epoch=1, loss=0.393]
 13%|█▎        | 2048/16000 [03:42<21:21, 10.89it/s, epoch=1, loss=0.352]
 13%|█▎        | 2049/16000 [03:42<21:21, 10.89it/s, epoch=1, loss=0.38] 
 13%|█▎        | 2050/16000 [03:42<22:02, 10.55it/s, epoch=1, loss=0.38]
 13%|█▎        | 2050/16000 [03:42<22:02, 10.55it/s, epoch=1, loss=0.369]
 13%|█▎        | 2051/16000 [03:42<22:02, 10.55it/s, epoch=1, loss=0.364]
 13%|█▎        | 2052/16000 [03:42<21:14, 10.95it/s, epoch=1, loss=0.364]
 13%|█▎        | 2052/16000 [03:42<21:14, 10.95it/s, epoch=1, loss=0.347]
 13%|█▎        | 2053/16000 [03:42<21:14, 10.95it/s, epoch=1, loss=0.375]
 13%|█▎        | 2054/16000 [03:42<21:44, 10.69it/s, epoch=1, loss=0.375]
 13%|█▎        | 2054/16000 [03:42<21:44, 10.69it/s, epoch=1, loss=0.345]
 13%|█▎        | 2055/16000 [03:42<21:44, 10.69it/s, epoch=1, loss=0.339]
 13%|█▎        | 2056/16000 [03:42<21:00, 11.06it/s, epoch=1, loss=0.339]
 13%|█▎        | 2056/16000 [03:42<21:00, 11.06it/s, epoch=1, loss=0.358]
 13%|█▎        | 2057/16000 [03:42<21:00, 11.06it/s, epoch=1, loss=0.35] 
 13%|█▎        | 2058/16000 [03:42<21:21, 10.88it/s, epoch=1, loss=0.35]
 13%|█▎        | 2058/16000 [03:42<21:21, 10.88it/s, epoch=1, loss=0.344]
 13%|█▎        | 2059/16000 [03:43<21:21, 10.88it/s, epoch=1, loss=0.336]
 13%|█▎        | 2060/16000 [03:43<20:27, 11.36it/s, epoch=1, loss=0.336]
 13%|█▎        | 2060/16000 [03:43<20:27, 11.36it/s, epoch=1, loss=0.377]
 13%|█▎        | 2061/16000 [03:43<20:27, 11.36it/s, epoch=1, loss=0.379]
 13%|█▎        | 2062/16000 [03:43<20:27, 11.35it/s, epoch=1, loss=0.379]
 13%|█▎        | 2062/16000 [03:43<20:27, 11.35it/s, epoch=1, loss=0.358]
 13%|█▎        | 2063/16000 [03:43<20:27, 11.35it/s, epoch=1, loss=0.349]
 13%|█▎        | 2064/16000 [03:43<19:51, 11.70it/s, epoch=1, loss=0.349]
 13%|█▎        | 2064/16000 [03:43<19:51, 11.70it/s, epoch=1, loss=0.329]
 13%|█▎        | 2065/16000 [03:43<19:50, 11.70it/s, epoch=1, loss=0.354]
 13%|█▎        | 2066/16000 [03:43<19:39, 11.82it/s, epoch=1, loss=0.354]
 13%|█▎        | 2066/16000 [03:43<19:39, 11.82it/s, epoch=1, loss=0.358]
 13%|█▎        | 2067/16000 [03:43<19:39, 11.82it/s, epoch=1, loss=0.333]
 13%|█▎        | 2068/16000 [03:43<19:26, 11.95it/s, epoch=1, loss=0.333]
 13%|█▎        | 2068/16000 [03:43<19:26, 11.95it/s, epoch=1, loss=0.333]
 13%|█▎        | 2069/16000 [03:43<19:26, 11.95it/s, epoch=1, loss=0.393]
 13%|█▎        | 2070/16000 [03:43<19:26, 11.94it/s, epoch=1, loss=0.393]
 13%|█▎        | 2070/16000 [03:43<19:26, 11.94it/s, epoch=1, loss=0.35] 
 13%|█▎        | 2071/16000 [03:44<19:26, 11.94it/s, epoch=1, loss=0.34]
 13%|█▎        | 2072/16000 [03:44<19:38, 11.81it/s, epoch=1, loss=0.34]
 13%|█▎        | 2072/16000 [03:44<19:38, 11.81it/s, epoch=1, loss=0.338]
 13%|█▎        | 2073/16000 [03:44<19:38, 11.81it/s, epoch=1, loss=0.367]
 13%|█▎        | 2074/16000 [03:44<23:01, 10.08it/s, epoch=1, loss=0.367]
 13%|█▎        | 2074/16000 [03:44<23:01, 10.08it/s, epoch=1, loss=0.357]
 13%|█▎        | 2075/16000 [03:44<23:01, 10.08it/s, epoch=1, loss=0.351]
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 13%|█▎        | 2076/16000 [03:44<22:13, 10.44it/s, epoch=1, loss=0.343]
 13%|█▎        | 2077/16000 [03:44<22:13, 10.44it/s, epoch=1, loss=0.362]
 13%|█▎        | 2078/16000 [03:44<21:57, 10.56it/s, epoch=1, loss=0.362]
 13%|█▎        | 2078/16000 [03:44<21:57, 10.56it/s, epoch=1, loss=0.342]
 13%|█▎        | 2079/16000 [03:44<21:57, 10.56it/s, epoch=1, loss=0.36] 
 13%|█▎        | 2080/16000 [03:44<21:12, 10.94it/s, epoch=1, loss=0.36]
 13%|█▎        | 2080/16000 [03:44<21:12, 10.94it/s, epoch=1, loss=0.354]
 13%|█▎        | 2081/16000 [03:45<21:12, 10.94it/s, epoch=1, loss=0.378]
 13%|█▎        | 2082/16000 [03:45<20:46, 11.16it/s, epoch=1, loss=0.378]
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 13%|█▎        | 2083/16000 [03:45<20:46, 11.16it/s, epoch=1, loss=0.345]
 13%|█▎        | 2084/16000 [03:45<20:46, 11.16it/s, epoch=1, loss=0.345]
 13%|█▎        | 2084/16000 [03:45<20:46, 11.16it/s, epoch=1, loss=0.36] 
 13%|█▎        | 2085/16000 [03:45<20:46, 11.16it/s, epoch=1, loss=0.361]
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 13%|█▎        | 2086/16000 [03:45<20:19, 11.41it/s, epoch=1, loss=0.362]
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+  plt.figure()
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Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Training finished:
+Seed 0, val loss 0.059148 at epoch 11
+Saved plots to ./act_ckpt/act-insert_tube/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Best ckpt, val loss 0.059148 @ epoch11
diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_wipe_board_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu3.log b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_wipe_board_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu3.log
new file mode 100644
index 0000000000000000000000000000000000000000..8626f23855e62ef291fd0b1f0c9d9640e04ecd64
--- /dev/null
+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/policy_wipe_board_train_config_vitacdreamer_real150_stage2_depthdelta_16k_gpu3.log
@@ -0,0 +1,49 @@
+/dev/shm/muse/envs/vitacdreamer_prior512/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
+  warnings.warn(
+/dev/shm/muse/envs/vitacdreamer_prior512/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
+  warnings.warn(msg)
+
+Data from: ./data/sim-wipe_board/real-150
+
+build_ACT_model_and_optimizer Namespace(lr=1e-05, lr_backbone=1e-05, batch_size=32, weight_decay=0.0001, epochs=300, lr_drop=200, clip_max_norm=0.1, dilation=False, pre_norm=False, vitacdreamer_cross_attn_layers='none', masks=False, eval=False, onscreen_render=False, num_epochs=1, state_dim=8, kl_weight=7.5, chunk_size=15, hidden_dim=512, dim_feedforward=3200, temporal_agg=True, device='cuda:0', ckpt_dir='./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k', policy_class='ACT', num_steps=16000, num_workers=4, save_freq=1000, position_embedding='sine', lr_vision_backbone=1e-05, vitacdreamer_adapter_lr=5e-05, backbone='resnet18', nheads=8, enc_layers=4, dec_layers=7, dropout=0.025, camera_names=['cam_high'], tactile_names=['tac_left', 'tac_right'], lr_tactile_backbone=1e-05, tactile_masks=False, tactile_backbone='resnet18', tactile_ckpt=None, tactile_dilation=False, use_vitacdreamer_feature=True, vitacdreamer_history_len=5, vitacdreamer_sample_stride=5, vitacdreamer_feature_dim=512, vitacdreamer_fusion_mode='feature_query_policy_kv', finetune_vitacdreamer_encoder=False, vitacdreamer_checkpoint='/dev/shm/muse/checkpoints/real_insert_tube_wipe_board_150_h32_20260725/stage2_prior512_hlen5_depthdelta_120train30val_b192_from_stage1_prior512_hlen5_120train30val_b192/stage2_v2_encoder_only.pth', vitacdreamer_feature_cache_dir='./data/sim-{task_stem}/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727', vitacdreamer_task_order=['insert_tube', 'wipe_board'], task_name='sim-wipe_board-real-150', config_path='./train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml', seed=0, distributed=False, rank=0, local_rank=0)
+Namespace(lr=1e-05, lr_backbone=1e-05, batch_size=32, weight_decay=0.0001, epochs=300, lr_drop=200, clip_max_norm=0.1, dilation=False, pre_norm=False, vitacdreamer_cross_attn_layers='none', masks=False, eval=False, onscreen_render=False, num_epochs=1, state_dim=8, kl_weight=7.5, chunk_size=15, hidden_dim=512, dim_feedforward=3200, temporal_agg=True, device='cuda:0', ckpt_dir='./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k', policy_class='ACT', num_steps=16000, num_workers=4, save_freq=1000, position_embedding='sine', lr_vision_backbone=1e-05, vitacdreamer_adapter_lr=5e-05, backbone='resnet18', nheads=8, enc_layers=4, dec_layers=7, dropout=0.025, camera_names=['cam_high'], tactile_names=['tac_left', 'tac_right'], lr_tactile_backbone=1e-05, tactile_masks=False, tactile_backbone='resnet18', tactile_ckpt=None, tactile_dilation=False, use_vitacdreamer_feature=True, vitacdreamer_history_len=5, vitacdreamer_sample_stride=5, vitacdreamer_feature_dim=512, vitacdreamer_fusion_mode='feature_query_policy_kv', finetune_vitacdreamer_encoder=False, vitacdreamer_checkpoint='/dev/shm/muse/checkpoints/real_insert_tube_wipe_board_150_h32_20260725/stage2_prior512_hlen5_depthdelta_120train30val_b192_from_stage1_prior512_hlen5_120train30val_b192/stage2_v2_encoder_only.pth', vitacdreamer_feature_cache_dir='./data/sim-{task_stem}/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727', vitacdreamer_task_order=['insert_tube', 'wipe_board'], task_name='sim-wipe_board-real-150', config_path='./train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml', seed=0, distributed=False, rank=0, local_rank=0)
+number of parameters: 100.95M
+KL Weight 7.5
+
  0%|          | 0/16000 [00:00<?, ?it/s]/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
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  7%|▋         | 1108/16000 [01:56<26:08,  9.49it/s, epoch=0, loss=0.47]Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+/dev/shm/muse/src/ViTacDreamer_policy/UniVTAC/policy/ACT/utils.py:291: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)
+  vitacdreamer_inputs = torch.from_numpy(
+
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 12%|█▏        | 1943/16000 [03:53<25:13,  9.29it/s, epoch=1, loss=0.39]
 12%|█▏        | 1943/16000 [03:53<25:13,  9.29it/s, epoch=1, loss=0.389]
 12%|█▏        | 1944/16000 [03:53<26:05,  8.98it/s, epoch=1, loss=0.389]
 12%|█▏        | 1944/16000 [03:53<26:05,  8.98it/s, epoch=1, loss=0.395]
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 12%|█▏        | 1950/16000 [03:54<25:30,  9.18it/s, epoch=1, loss=0.383]
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 12%|█▏        | 1953/16000 [03:54<25:56,  9.02it/s, epoch=1, loss=0.393]
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 12%|█▏        | 1954/16000 [03:55<24:52,  9.41it/s, epoch=1, loss=0.35] 
 12%|█▏        | 1955/16000 [03:55<24:43,  9.47it/s, epoch=1, loss=0.35]
 12%|█▏        | 1955/16000 [03:55<24:43,  9.47it/s, epoch=1, loss=0.389]
 12%|█▏        | 1956/16000 [03:55<24:43,  9.47it/s, epoch=1, loss=0.407]
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 12%|█▏        | 1957/16000 [03:55<23:53,  9.80it/s, epoch=1, loss=0.4]  
 12%|█▏        | 1958/16000 [03:55<23:53,  9.80it/s, epoch=1, loss=0.394]
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 12%|█▏        | 1959/16000 [03:55<22:56, 10.20it/s, epoch=1, loss=0.398]
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 12%|█▏        | 1965/16000 [03:56<24:30,  9.55it/s, epoch=1, loss=0.412]
 12%|█▏        | 1965/16000 [03:56<24:30,  9.55it/s, epoch=1, loss=0.389]
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 12%|█▏        | 1966/16000 [03:56<25:08,  9.31it/s, epoch=1, loss=0.394]
 12%|█▏        | 1967/16000 [03:56<25:28,  9.18it/s, epoch=1, loss=0.394]
 12%|█▏        | 1967/16000 [03:56<25:28,  9.18it/s, epoch=1, loss=0.393]
 12%|█▏        | 1968/16000 [03:56<25:16,  9.25it/s, epoch=1, loss=0.393]
 12%|█▏        | 1968/16000 [03:56<25:16,  9.25it/s, epoch=1, loss=0.37] 
 12%|█▏        | 1969/16000 [03:56<25:16,  9.25it/s, epoch=1, loss=0.389]
 12%|█▏        | 1970/16000 [03:56<23:59,  9.75it/s, epoch=1, loss=0.389]
 12%|█▏        | 1970/16000 [03:56<23:59,  9.75it/s, epoch=1, loss=0.379]
 12%|█▏        | 1971/16000 [03:56<23:59,  9.75it/s, epoch=1, loss=0.404]
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 12%|█▏        | 1973/16000 [03:56<23:20, 10.02it/s, epoch=1, loss=0.372]
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 12%|█▏        | 1974/16000 [03:57<22:47, 10.26it/s, epoch=1, loss=0.368]
 12%|█▏        | 1975/16000 [03:57<22:47, 10.26it/s, epoch=1, loss=0.387]
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 12%|█▏        | 1978/16000 [03:57<21:37, 10.80it/s, epoch=1, loss=0.371]
 12%|█▏        | 1979/16000 [03:57<21:37, 10.80it/s, epoch=1, loss=0.366]
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 12%|█▏        | 1980/16000 [03:57<24:36,  9.49it/s, epoch=1, loss=0.378]
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 12%|█▏        | 1982/16000 [03:57<26:26,  8.83it/s, epoch=1, loss=0.357]
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 12%|█▏        | 1983/16000 [03:58<27:36,  8.46it/s, epoch=1, loss=0.392]
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 12%|█▏        | 1985/16000 [03:58<31:06,  7.51it/s, epoch=1, loss=0.411]
 12%|█▏        | 1985/16000 [03:58<31:06,  7.51it/s, epoch=1, loss=0.381]
 12%|█▏        | 1986/16000 [03:58<33:30,  6.97it/s, epoch=1, loss=0.381]
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 12%|█▏        | 1987/16000 [03:58<34:29,  6.77it/s, epoch=1, loss=0.38]
 12%|█▏        | 1987/16000 [03:58<34:29,  6.77it/s, epoch=1, loss=0.407]
 12%|█▏        | 1988/16000 [03:58<35:47,  6.52it/s, epoch=1, loss=0.407]
 12%|█▏        | 1988/16000 [03:58<35:47,  6.52it/s, epoch=1, loss=0.387]
 12%|█▏        | 1989/16000 [03:58<34:12,  6.83it/s, epoch=1, loss=0.387]
 12%|█▏        | 1989/16000 [03:58<34:12,  6.83it/s, epoch=1, loss=0.366]
 12%|█▏        | 1990/16000 [03:59<34:12,  6.83it/s, epoch=1, loss=0.358]
 12%|█▏        | 1991/16000 [03:59<28:32,  8.18it/s, epoch=1, loss=0.358]
 12%|█▏        | 1991/16000 [03:59<28:32,  8.18it/s, epoch=1, loss=0.368]
 12%|█▏        | 1992/16000 [03:59<28:31,  8.18it/s, epoch=1, loss=0.368]
 12%|█▏        | 1992/16000 [03:59<28:31,  8.18it/s, epoch=1, loss=0.379]
 12%|█▏        | 1993/16000 [03:59<28:21,  8.23it/s, epoch=1, loss=0.379]
 12%|█▏        | 1993/16000 [03:59<28:21,  8.23it/s, epoch=1, loss=0.393]
 12%|█▏        | 1994/16000 [03:59<28:21,  8.23it/s, epoch=1, loss=0.383]
 12%|█▏        | 1995/16000 [03:59<25:28,  9.16it/s, epoch=1, loss=0.383]
 12%|█▏        | 1995/16000 [03:59<25:28,  9.16it/s, epoch=1, loss=0.364]
 12%|█▏        | 1996/16000 [03:59<25:28,  9.16it/s, epoch=1, loss=0.382]
 12%|█▏        | 1997/16000 [03:59<23:22,  9.98it/s, epoch=1, loss=0.382]
 12%|█▏        | 1997/16000 [03:59<23:22,  9.98it/s, epoch=1, loss=0.371]
 12%|█▏        | 1998/16000 [03:59<23:22,  9.98it/s, epoch=1, loss=0.394]
 12%|█▏        | 1999/16000 [03:59<22:07, 10.54it/s, epoch=1, loss=0.394]
 12%|█▏        | 1999/16000 [03:59<22:07, 10.54it/s, epoch=1, loss=0.363]
 12%|█▎        | 2000/16000 [04:00<22:07, 10.54it/s, epoch=1, loss=0.378]
 13%|█▎        | 2001/16000 [04:00<56:04,  4.16it/s, epoch=1, loss=0.378]
 13%|█▎        | 2001/16000 [04:00<56:04,  4.16it/s, epoch=1, loss=0.381]
 13%|█▎        | 2002/16000 [04:01<56:04,  4.16it/s, epoch=1, loss=0.396]
 13%|█▎        | 2003/16000 [04:01<44:50,  5.20it/s, epoch=1, loss=0.396]
 13%|█▎        | 2003/16000 [04:01<44:50,  5.20it/s, epoch=1, loss=0.394]
 13%|█▎        | 2004/16000 [04:01<44:50,  5.20it/s, epoch=1, loss=0.364]
 13%|█▎        | 2005/16000 [04:01<37:27,  6.23it/s, epoch=1, loss=0.364]
 13%|█▎        | 2005/16000 [04:01<37:27,  6.23it/s, epoch=1, loss=0.364]
 13%|█▎        | 2006/16000 [04:01<37:27,  6.23it/s, epoch=1, loss=0.388]
 13%|█▎        | 2007/16000 [04:01<32:39,  7.14it/s, epoch=1, loss=0.388]
 13%|█▎        | 2007/16000 [04:01<32:39,  7.14it/s, epoch=1, loss=0.395]
 13%|█▎        | 2008/16000 [04:01<32:39,  7.14it/s, epoch=1, loss=0.361]
 13%|█▎        | 2009/16000 [04:01<30:19,  7.69it/s, epoch=1, loss=0.361]
 13%|█▎        | 2009/16000 [04:01<30:19,  7.69it/s, epoch=1, loss=0.366]
 13%|█▎        | 2010/16000 [04:01<30:10,  7.73it/s, epoch=1, loss=0.366]
 13%|█▎        | 2010/16000 [04:01<30:10,  7.73it/s, epoch=1, loss=0.388]
 13%|█▎        | 2011/16000 [04:01<29:56,  7.79it/s, epoch=1, loss=0.388]
 13%|█▎        | 2011/16000 [04:02<29:56,  7.79it/s, epoch=1, loss=0.37] 
 13%|█▎        | 2012/16000 [04:02<29:27,  7.91it/s, epoch=1, loss=0.37]
 13%|█▎        | 2012/16000 [04:02<29:27,  7.91it/s, epoch=1, loss=0.341]
 13%|█▎        | 2013/16000 [04:02<29:27,  7.91it/s, epoch=1, loss=0.365]
 13%|█▎        | 2014/16000 [04:02<27:03,  8.62it/s, epoch=1, loss=0.365]
 13%|█▎        | 2014/16000 [04:02<27:03,  8.62it/s, epoch=1, loss=0.397]
 13%|█▎        | 2015/16000 [04:02<27:07,  8.60it/s, epoch=1, loss=0.397]
 13%|█▎        | 2015/16000 [04:02<27:07,  8.60it/s, epoch=1, loss=0.401]
 13%|█▎        | 2016/16000 [04:02<27:06,  8.60it/s, epoch=1, loss=0.351]
 13%|█▎        | 2017/16000 [04:02<24:23,  9.55it/s, epoch=1, loss=0.351]
 13%|█▎        | 2017/16000 [04:02<24:23,  9.55it/s, epoch=1, loss=0.376]
 13%|█▎        | 2018/16000 [04:02<24:23,  9.55it/s, epoch=1, loss=0.377]
 13%|█▎        | 2019/16000 [04:02<23:11, 10.05it/s, epoch=1, loss=0.377]
 13%|█▎        | 2019/16000 [04:02<23:11, 10.05it/s, epoch=1, loss=0.363]
 13%|█▎        | 2020/16000 [04:02<23:11, 10.05it/s, epoch=1, loss=0.364]
 13%|█▎        | 2021/16000 [04:02<22:19, 10.43it/s, epoch=1, loss=0.364]
 13%|█▎        | 2021/16000 [04:02<22:19, 10.43it/s, epoch=1, loss=0.369]
 13%|█▎        | 2022/16000 [04:03<22:19, 10.43it/s, epoch=1, loss=0.379]
 13%|█▎        | 2023/16000 [04:03<22:11, 10.49it/s, epoch=1, loss=0.379]
 13%|█▎        | 2023/16000 [04:03<22:11, 10.49it/s, epoch=1, loss=0.365]
 13%|█▎        | 2024/16000 [04:03<22:11, 10.49it/s, epoch=1, loss=0.358]
 13%|█▎        | 2025/16000 [04:03<23:29,  9.92it/s, epoch=1, loss=0.358]
 13%|█▎        | 2025/16000 [04:03<23:29,  9.92it/s, epoch=1, loss=0.354]
 13%|█▎        | 2026/16000 [04:03<23:29,  9.92it/s, epoch=1, loss=0.376]
 13%|█▎        | 2027/16000 [04:03<25:13,  9.24it/s, epoch=1, loss=0.376]
 13%|█▎        | 2027/16000 [04:03<25:13,  9.24it/s, epoch=1, loss=0.368]
 13%|█▎        | 2028/16000 [04:03<25:12,  9.24it/s, epoch=1, loss=0.369]
 13%|█▎        | 2029/16000 [04:03<23:59,  9.70it/s, epoch=1, loss=0.369]
 13%|█▎        | 2029/16000 [04:03<23:59,  9.70it/s, epoch=1, loss=0.365]
 13%|█▎        | 2030/16000 [04:03<23:59,  9.70it/s, epoch=1, loss=0.371]
 13%|█▎        | 2031/16000 [04:03<23:09, 10.05it/s, epoch=1, loss=0.371]
 13%|█▎        | 2031/16000 [04:04<23:09, 10.05it/s, epoch=1, loss=0.379]
 13%|█▎        | 2032/16000 [04:04<23:09, 10.05it/s, epoch=1, loss=0.392]
 13%|█▎        | 2033/16000 [04:04<22:40, 10.26it/s, epoch=1, loss=0.392]
 13%|█▎        | 2033/16000 [04:04<22:40, 10.26it/s, epoch=1, loss=0.362]
 13%|█▎        | 2034/16000 [04:04<22:40, 10.26it/s, epoch=1, loss=0.393]
 13%|█▎        | 2035/16000 [04:04<22:31, 10.33it/s, epoch=1, loss=0.393]
 13%|█▎        | 2035/16000 [04:04<22:31, 10.33it/s, epoch=1, loss=0.393]
 13%|█▎        | 2036/16000 [04:04<22:31, 10.33it/s, epoch=1, loss=0.366]
 13%|█▎        | 2037/16000 [04:04<24:02,  9.68it/s, epoch=1, loss=0.366]
 13%|█▎        | 2037/16000 [04:04<24:02,  9.68it/s, epoch=1, loss=0.376]
 13%|█▎        | 2038/16000 [04:04<24:30,  9.49it/s, epoch=1, loss=0.376]
 13%|█▎        | 2038/16000 [04:04<24:30,  9.49it/s, epoch=1, loss=0.375]
 13%|█▎        | 2039/16000 [04:04<24:54,  9.34it/s, epoch=1, loss=0.375]
 13%|█▎        | 2039/16000 [04:04<24:54,  9.34it/s, epoch=1, loss=0.393]
 13%|█▎        | 2040/16000 [04:04<25:25,  9.15it/s, epoch=1, loss=0.393]
 13%|█▎        | 2040/16000 [04:04<25:25,  9.15it/s, epoch=1, loss=0.344]
 13%|█▎        | 2041/16000 [04:05<25:25,  9.15it/s, epoch=1, loss=0.363]
 13%|█▎        | 2042/16000 [04:05<23:25,  9.93it/s, epoch=1, loss=0.363]
 13%|█▎        | 2042/16000 [04:05<23:25,  9.93it/s, epoch=1, loss=0.369]
 13%|█▎        | 2043/16000 [04:05<23:25,  9.93it/s, epoch=1, loss=0.368]
 13%|█▎        | 2044/16000 [04:05<22:45, 10.22it/s, epoch=1, loss=0.368]
 13%|█▎        | 2044/16000 [04:05<22:45, 10.22it/s, epoch=1, loss=0.376]
 13%|█▎        | 2045/16000 [04:05<22:45, 10.22it/s, epoch=1, loss=0.376]
 13%|█▎        | 2046/16000 [04:05<23:29,  9.90it/s, epoch=1, loss=0.376]
 13%|█▎        | 2046/16000 [04:05<23:29,  9.90it/s, epoch=1, loss=0.362]
 13%|█▎        | 2047/16000 [04:05<23:55,  9.72it/s, epoch=1, loss=0.362]
 13%|█▎        | 2047/16000 [04:05<23:55,  9.72it/s, epoch=1, loss=0.385]
 13%|█▎        | 2048/16000 [04:05<24:20,  9.55it/s, epoch=1, loss=0.385]
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+  plt.figure()
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Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Training finished:
+Seed 0, val loss 0.068180 at epoch 8
+Saved plots to ./act_ckpt/act-wipe_board/real-150/train_config_vitacdreamer_real150_stage2_depthdelta_16k
+Best ckpt, val loss 0.068180 @ epoch8
diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_insert_tube_gpu2.log b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_insert_tube_gpu2.log
new file mode 100644
index 0000000000000000000000000000000000000000..fa819bbbf93e6aca43c20192a935b37e4ee424fa
--- /dev/null
+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_insert_tube_gpu2.log
@@ -0,0 +1,789 @@
+/dev/shm/muse/src/ViTacDreamer_policy/vitacdreamer/policy_wrapper.py:91: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  checkpoint = torch.load(checkpoint_path, map_location=device)
+
Loading weights:   0%|          | 0/198 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 198/198 [00:00<00:00, 9065.60it/s]
+[transformers] ViTModel LOAD REPORT from: /dev/shm/muse/src/ViTacDreamer_policy/.hf_cache/google_vit_base_patch16_224
+Key                 | Status     | 
+--------------------+------------+-
+classifier.bias     | UNEXPECTED | 
+classifier.weight   | UNEXPECTED | 
+pooler.dense.weight | MISSING    | 
+pooler.dense.bias   | MISSING    | 
+
+Notes:
+- UNEXPECTED:	can be ignored when loading from different task/architecture; not ok if you expect identical arch.
+- MISSING:	those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.
+
Loading weights:   0%|          | 0/198 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 198/198 [00:00<00:00, 8615.21it/s]
+[transformers] ViTModel LOAD REPORT from: /dev/shm/muse/src/ViTacDreamer_policy/.hf_cache/google_vit_base_patch16_224
+Key                 | Status     | 
+--------------------+------------+-
+classifier.bias     | UNEXPECTED | 
+classifier.weight   | UNEXPECTED | 
+pooler.dense.weight | MISSING    | 
+pooler.dense.bias   | MISSING    | 
+
+Notes:
+- UNEXPECTED:	can be ignored when loading from different task/architecture; not ok if you expect identical arch.
+- MISSING:	those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.
+
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+
                                                               
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+Saved cached ViTacDreamer features to data/sim-insert_tube/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727
diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_wipe_board_gpu3.log b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_wipe_board_gpu3.log
new file mode 100644
index 0000000000000000000000000000000000000000..bd43f34ece2bc6b70cea300d7ef05a271a08e1de
--- /dev/null
+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/logs/precompute_wipe_board_gpu3.log
@@ -0,0 +1,742 @@
+/dev/shm/muse/src/ViTacDreamer_policy/vitacdreamer/policy_wrapper.py:91: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
+  checkpoint = torch.load(checkpoint_path, map_location=device)
+
Loading weights:   0%|          | 0/198 [00:00<?, ?it/s]
Loading weights: 100%|██████████| 198/198 [00:00<00:00, 9574.82it/s]
+[transformers] ViTModel LOAD REPORT from: /dev/shm/muse/src/ViTacDreamer_policy/.hf_cache/google_vit_base_patch16_224
+Key                 | Status     | 
+--------------------+------------+-
+classifier.bias     | UNEXPECTED | 
+classifier.weight   | UNEXPECTED | 
+pooler.dense.bias   | MISSING    | 
+pooler.dense.weight | MISSING    | 
+
+Notes:
+- UNEXPECTED:	can be ignored when loading from different task/architecture; not ok if you expect identical arch.
+- MISSING:	those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.
+
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Loading weights: 100%|██████████| 198/198 [00:00<00:00, 9061.84it/s]
+[transformers] ViTModel LOAD REPORT from: /dev/shm/muse/src/ViTacDreamer_policy/.hf_cache/google_vit_base_patch16_224
+Key                 | Status     | 
+--------------------+------------+-
+classifier.bias     | UNEXPECTED | 
+classifier.weight   | UNEXPECTED | 
+pooler.dense.bias   | MISSING    | 
+pooler.dense.weight | MISSING    | 
+
+Notes:
+- UNEXPECTED:	can be ignored when loading from different task/architecture; not ok if you expect identical arch.
+- MISSING:	those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.
+
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+Saved cached ViTacDreamer features to data/sim-wipe_board/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727
diff --git a/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml
new file mode 100644
index 0000000000000000000000000000000000000000..dbb592e8671273c2481e199ff0796342c52af35a
--- /dev/null
+++ b/checkpoints/h100/real150_vitacdreamer_encoder_policy_20260727/train_config_vitacdreamer_real150_stage2_depthdelta_16k.yml
@@ -0,0 +1,54 @@
+# Real-robot ACT + cached frozen ViTacDreamer Stage2 depth-delta feature-query policy, 150 demos/task.
+state_dim: 8
+kl_weight: 7.5
+chunk_size: 15
+hidden_dim: 512
+dim_feedforward: 3200
+
+temporal_agg: true
+device: cuda:0
+
+ckpt_dir: null
+policy_class: ACT
+num_steps: 16000
+
+batch_size: 32
+num_workers: 4
+save_freq: 1000
+position_embedding: sine
+lr_vision_backbone: 0.00001
+weight_decay: 0.0001
+lr: 0.00001
+vitacdreamer_adapter_lr: 0.00005
+masks: false
+dilation: false
+backbone: resnet18
+nheads: 8
+enc_layers: 4
+dec_layers: 7
+pre_norm: false
+dropout: 0.025
+camera_names:
+  - cam_high
+
+tactile_names:
+  - tac_left
+  - tac_right
+lr_tactile_backbone: 0.00001
+tactile_masks: false
+tactile_backbone: resnet18
+tactile_ckpt: null
+tactile_dilation: false
+
+use_vitacdreamer_feature: true
+vitacdreamer_history_len: 5
+vitacdreamer_sample_stride: 5
+vitacdreamer_feature_dim: 512
+vitacdreamer_fusion_mode: feature_query_policy_kv
+vitacdreamer_cross_attn_layers: none
+finetune_vitacdreamer_encoder: false
+vitacdreamer_checkpoint: /dev/shm/muse/checkpoints/real_insert_tube_wipe_board_150_h32_20260725/stage2_prior512_hlen5_depthdelta_120train30val_b192_from_stage1_prior512_hlen5_120train30val_b192/stage2_v2_encoder_only.pth
+vitacdreamer_feature_cache_dir: ./data/sim-{task_stem}/real-150/vitacdreamer_features_real150_stage2_depthdelta_20260727
+vitacdreamer_task_order:
+  - insert_tube
+  - wipe_board