Add Rel-AE DTW-Sammon/UnitZ/DistanceOnly checkpoint-60000

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/config.json

@@ -0,0 +1,88 @@
+{
+  "action_horizon": 40,
+  "add_pos_embed": true,
+  "apply_sincos_state_encoding": false,
+  "architectures": [
+    "Gr00tN1d7"
+  ],
+  "attn_dropout": 0.2,
+  "attn_implementation": null,
+  "backbone_embedding_dim": 2048,
+  "backbone_trainable_params_fp32": true,
+  "color_jitter_params": {
+    "brightness": 0.3,
+    "contrast": 0.4,
+    "hue": 0.08,
+    "saturation": 0.5
+  },
+  "crop_fraction": 0.95,
+  "diffusion_model_cfg": {
+    "attention_head_dim": 48,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "interleave_self_attention": true,
+    "norm_type": "ada_norm",
+    "num_attention_heads": 32,
+    "num_layers": 32,
+    "output_dim": 1024,
+    "positional_embeddings": null
+  },
+  "dtype": "float32",
+  "exclude_state": false,
+  "formalize_language": true,
+  "hidden_size": 1024,
+  "image_crop_size": [
+    230,
+    230
+  ],
+  "image_target_size": [
+    256,
+    256
+  ],
+  "letter_box_transform": false,
+  "load_bf16": false,
+  "max_action_dim": 132,
+  "max_num_embodiments": 32,
+  "max_seq_len": 1024,
+  "max_state_dim": 132,
+  "model_dtype": "bfloat16",
+  "model_name": "nvidia/Cosmos-Reason2-2B",
+  "model_type": "Gr00tN1d7",
+  "noise_beta_alpha": 1.5,
+  "noise_beta_beta": 1.0,
+  "noise_s": 0.999,
+  "num_inference_timesteps": 4,
+  "num_timestep_buckets": 1000,
+  "random_history_crop": true,
+  "random_rotation_angle": 0,
+  "reproject_vision": false,
+  "rtc_ramp_rate": 6.0,
+  "select_layer": 16,
+  "shortest_image_edge": 256,
+  "state_dropout_prob": 0.0,
+  "state_gaussian_noise_std": 0.0,
+  "transformers_version": "4.57.3",
+  "tune_diffusion_model": true,
+  "tune_linear": true,
+  "tune_llm": false,
+  "tune_projector": true,
+  "tune_top_llm_layers": 0,
+  "tune_visual": false,
+  "tune_vlln": true,
+  "use_albumentations": true,
+  "use_alternate_vl_dit": true,
+  "use_flash_attention": true,
+  "use_future_tokens": false,
+  "use_mean_std": false,
+  "use_percentiles": true,
+  "use_vl_self_attention": true,
+  "use_vlln": true,
+  "vl_self_attention_cfg": {
+    "attention_head_dim": 64,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "num_attention_heads": 32,
+    "num_layers": 4,
+    "positional_embeddings": null
+  }
+}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/dataset_state.json

@@ -0,0 +1 @@
+{"epoch": 0, "curr_shard_index": null}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/embodiment_id.json

@@ -0,0 +1,57 @@
+{
+  "robocasa_panda_omron": 13,
+  "oxe_droid": 17,
+  "oxe_fractal": 18,
+  "oxe_language_table": 19,
+  "oxe_bridge": 20,
+  "unknown": 22,
+  "gr1_unified": 20,
+  "agibot": 26,
+  "sim_behavior_r1_pro": 23,
+  "xdof": 24,
+  "xdof_oss_data": 25,
+  "unitree_g1_full_body_with_waist_height_nav_cmd": 25,
+  "real_r1_pro_sharpa": 27,
+  "real_r1_pro_sharpa_add_view": 27,
+  "real_r1_pro_sharpa_relative_arm_joint": 26,
+  "real_r1_pro_sharpa_delta_eef": 26,
+  "real_r1_pro_sharpa_absolute_eef": 26,
+  "real_r1_pro_sharpa_meanstd": 26,
+  "real_r1_pro_sharpa_relative_eef": 26,
+  "real_r1_pro_sharpa_relative_eef_add_view": 26,
+  "real_r1_pro_sharpa_relative_eef_relative_hand": 26,
+  "real_r1_pro_sharpa_relative_eef_human": 26,
+  "real_r1_pro_sharpa_relative_eef_human_add_view": 26,
+  "real_r1_pro_sharpa_relative_eef_human_relative_hand": 26,
+  "real_r1_pro_sharpa_relative_eef_egodex": 26,
+  "real_r1_pro_sharpa_relative_eef_egodex_relative_hand": 26,
+  "real_r1_pro_sharpa_relative_eef_egodex_wrist_only": 26,
+  "real_r1_pro_sharpa_relative_eef_maxinsights": 26,
+  "real_r1_pro_sharpa_relative_eef_maxinsights_relative_hand": 26,
+  "real_r1_pro_sharpa_relative_eef_mecka": 26,
+  "real_r1_pro_sharpa_relative_eef_mecka_relative_hand": 26,
+  "real_g1_relative_eef_absolute_joints": 25,
+  "real_g1_relative_eef_absolute_joints_wrist_cam": 25,
+  "real_g1_relative_eef_relative_joints": 25,
+  "real_r1_pro_sharpa_relative_eef_relative_hand_relative_joint": 26,
+  "real_r1_pro_sharpa_relative_joint": 29,
+  "oxe_droid_relative_eef_relative_joint": 24,
+  "oxe_droid_relative_eef_relative_joint_swapped": 24,
+  "oxe_droid_relative_eef_relative_joint_upweight_z": 24,
+  "oxe_droid_relative_eef_relative_joint_upweight_z_swapped": 24,
+  "oxe_droid_relative_eef_relative_joint_3view": 24,
+  "oxe_droid_relative_eef_relative_joint_3view_swapped": 24,
+  "oxe_droid_relative_eef": 24,
+  "oxe_droid_joint_position_relative": 24,
+  "xdof_relative_eef_relative_joint": 27,
+  "xdof_relative_eef_relative_joint_subtask": 27,
+  "xdof_relative_eef": 27,
+  "xdof_relative_joint": 28,
+  "simpler_env_google": 0,
+  "simpler_env_widowx": 1,
+  "libero_sim": 2,
+  "droid_sim": 3,
+  "robocasa_gr1_tabletop": 10,
+  "new_embodiment": 10,
+  "unitree_g1_sonic": 11
+}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/experiment_cfg/conf.yaml

@@ -0,0 +1,234 @@
+load_config_path: null
+model:
+  model_type: Gr00tN1d7
+  model_dtype: bfloat16
+  model_name: nvidia/Cosmos-Reason2-2B
+  backbone_model_type: qwen
+  model_revision: null
+  tune_top_llm_layers: 0
+  backbone_embedding_dim: 2048
+  tune_llm: false
+  tune_visual: false
+  select_layer: 12
+  reproject_vision: false
+  use_flash_attention: true
+  load_bf16: false
+  backbone_trainable_params_fp32: true
+  image_crop_size:
+  - 230
+  - 230
+  image_target_size:
+  - 256
+  - 256
+  shortest_image_edge: null
+  crop_fraction: null
+  random_rotation_angle: null
+  color_jitter_params: null
+  use_albumentations_transforms: true
+  letter_box_transform: false
+  extra_augmentation_config: null
+  formalize_language: true
+  apply_sincos_state_encoding: false
+  use_percentiles: true
+  use_relative_action: true
+  max_state_dim: 132
+  max_action_dim: 132
+  action_horizon: 40
+  hidden_size: 1024
+  input_embedding_dim: 1536
+  state_history_length: 1
+  add_pos_embed: true
+  attn_dropout: 0.2
+  use_vlln: true
+  max_seq_len: 1024
+  use_alternate_vl_dit: true
+  attend_text_every_n_blocks: 2
+  diffusion_model_cfg:
+    positional_embeddings: null
+    num_layers: 16
+    num_attention_heads: 32
+    attention_head_dim: 48
+    norm_type: ada_norm
+    dropout: 0.2
+    final_dropout: true
+    output_dim: 1024
+    interleave_self_attention: true
+  num_inference_timesteps: 4
+  noise_beta_alpha: 1.5
+  noise_beta_beta: 1.0
+  noise_s: 0.999
+  num_timestep_buckets: 1000
+  tune_projector: true
+  tune_diffusion_model: true
+  tune_vlln: true
+  state_dropout_prob: 0.0
+  exclude_state: false
+  use_mean_std: false
+  max_num_embodiments: 32
+data:
+  datasets:
+  - dataset_paths:
+    - /home/junhyeong/robocasa_mg_gr00t_300
+    embodiment_tag: new_embodiment
+    mix_ratio: 1.0
+    dataset_type: physical_embodiment
+    val_dataset_path: null
+  modality_configs:
+    new_embodiment:
+      video:
+        delta_indices:
+        - 0
+        modality_keys:
+        - left_view
+        - right_view
+        - wrist_view
+        sin_cos_embedding_keys: null
+        mean_std_embedding_keys: null
+        action_configs: null
+      state:
+        delta_indices:
+        - 0
+        modality_keys:
+        - end_effector_position_relative
+        - end_effector_rotation_relative
+        - gripper_qpos
+        - base_position
+        - base_rotation
+        sin_cos_embedding_keys: null
+        mean_std_embedding_keys: null
+        action_configs: null
+      action:
+        delta_indices:
+        - 0
+        - 1
+        - 2
+        - 3
+        - 4
+        - 5
+        - 6
+        - 7
+        - 8
+        - 9
+        - 10
+        - 11
+        - 12
+        - 13
+        - 14
+        - 15
+        modality_keys:
+        - end_effector_position
+        - end_effector_rotation
+        - gripper_close
+        - base_motion
+        - control_mode
+        sin_cos_embedding_keys: null
+        mean_std_embedding_keys: null
+        action_configs:
+        - rep: DELTA
+          type: EEF
+          format: DEFAULT
+          state_key: null
+        - rep: DELTA
+          type: EEF
+          format: DEFAULT
+          state_key: null
+        - rep: ABSOLUTE
+          type: NON_EEF
+          format: DEFAULT
+          state_key: null
+        - rep: DELTA
+          type: NON_EEF
+          format: DEFAULT
+          state_key: null
+        - rep: ABSOLUTE
+          type: NON_EEF
+          format: DEFAULT
+          state_key: null
+      language:
+        delta_indices:
+        - 0
+        modality_keys:
+        - annotation.human.action.task_description
+        sin_cos_embedding_keys: null
+        mean_std_embedding_keys: null
+        action_configs: null
+  download_cache: false
+  shard_size: 1024
+  episode_sampling_rate: 0.1
+  num_shards_per_epoch: 100000
+  ds_weights_alpha: null
+  override_pretraining_statistics: true
+  mode: single_turn
+  random_chop: 0.0
+  mock_dataset_mode: false
+  shuffle: true
+  seed: 42
+  multiprocessing_context: fork
+  allow_padding: false
+  subsample_ratio: 1.0
+  image_crop_size:
+  - 244
+  - 244
+  image_target_size:
+  - 224
+  - 224
+training:
+  output_dir: /home/junhyeong/groot_robocasa-kitchen/train_ckpt/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly
+  experiment_name: DTWSammon_UnitZ_DistanceOnly
+  max_steps: 60000
+  global_batch_size: 64
+  per_gpu_batch_size: null
+  gradient_accumulation_steps: 1
+  learning_rate: 0.0001
+  lr_scheduler_type: cosine
+  weight_decay: 1.0e-05
+  warmup_ratio: 0.05
+  warmup_steps: 0
+  max_grad_norm: 1.0
+  optim: adamw_torch
+  start_from_checkpoint: nvidia/GR00T-N1.7-3B
+  skip_weight_loading: false
+  tf32: true
+  fp16: false
+  bf16: true
+  eval_bf16: true
+  logging_steps: 10
+  save_steps: 5000
+  save_total_limit: 1
+  save_vl_model: false
+  save_only_model: false
+  resume_from_checkpoint: false
+  upload_checkpoints: false
+  upload_every: 1000
+  upload_last_n_checkpoints: 5
+  max_concurrent_uploads: 2
+  eval_strategy: 'no'
+  eval_steps: 500
+  eval_set_split_ratio: 0.1
+  eval_batch_size: 2
+  save_best_eval_metric_name: ''
+  save_best_eval_metric_greater_is_better: true
+  deepspeed_stage: 2
+  gradient_checkpointing: false
+  transformers_trust_remote_code: true
+  transformers_local_files_only: false
+  transformers_cache_dir: null
+  transformers_access_token: null
+  use_ddp: false
+  ddp_bucket_cap_mb: 100
+  num_gpus: 1
+  dataloader_num_workers: 2
+  remove_unused_columns: false
+  use_wandb: true
+  wandb_project: finetune-gr00t-n1d7
+  enable_profiling: false
+  max_retries: 3
+  assert_loss_less_than: null
+  add_rl_callback: false
+  enable_open_loop_eval: false
+  open_loop_eval_traj_ids:
+  - 0
+  open_loop_eval_steps_per_traj: 100
+  open_loop_eval_plot_indices: null
+max_steps: 60000
+save_steps: 5000

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/experiment_cfg/config.yaml

@@ -0,0 +1,116 @@
+# RoboCasa-Kitchen (v2) finetune with FlatAE-DTW-Sammon lambda=0.1 unit_z distance-only RKD teacher.
+#
+# Run:
+#   cd $PROJECT
+#   export LD_LIBRARY_PATH="$FFMPEG_LIB:$LD_LIBRARY_PATH"
+#   CUDA_VISIBLE_DEVICES=1 uv run python clvla/launch_finetune.py --config clvla/cfg/Robocasa-kitchen/Rel-AE/RKD_RawMu_DistanceOnly.yaml
+#
+# Any flag can be overridden on the CLI, e.g. append `--max-steps 1000`.
+# Keys are stock finetune flags (underscored); the `rkd:` block maps to --rkd-*.
+# ${HOME} and other env vars are expanded.
+
+# --- stock finetune flags ---
+base_model_path: nvidia/GR00T-N1.7-3B
+dataset_path: ${HOME}/robocasa_mg_gr00t_300
+embodiment_tag: new_embodiment
+modality_config_path: ${HOME}/clvla/benchmarks/Robocasa-Kitchen/robocasa_config.py
+output_dir: ${HOME}/groot_robocasa-kitchen/train_ckpt/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly
+experiment_name: DTWSammon_UnitZ_DistanceOnly
+global_batch_size: 64
+max_steps: 60000
+save_steps: 5000
+save_total_limit: 1
+num_gpus: 1            # RKD is single-GPU only
+# learning_rate: 1.0e-4
+# warmup_ratio: 0.05
+state_dropout_prob: 0.0
+use_percentiles: true
+use_wandb: true
+wandb_project: finetune-gr00t-n1d7
+
+# --- RKD add-on (maps to --rkd-*) ---
+rkd:
+  enabled: true
+  teacher_checkpoint: ${HOME}/groot_robocasa_V2/RelVAE_ckpt/mg_gr00t_300/flat_ae_dtw_z32_repraw_z_fh128_dh512_gw0p1/checkpoint-step-20000
+  latent_mode: unit_z
+  fm_weight: 1.0
+  loss_weight: 1.0
+  loss_weight_end: 0.0        # cosine ramp 1 -> 0 over max_steps
+  loss_weight_schedule: cosine
+  distance_weight: 1.0
+  angle_weight: 0.0
+  student_hidden: 512
+  student_output: 512
+  # max_steps omitted -> inherits --max-steps for the schedule denominator
+#
+# --- RKD student head 연결 지점 ---
+# RKD on이면 StudentAttentionHead(in=2048 backbone_embedding_dim, hidden/out=512)
+# 1개가 trainable로 추가된다(teacher FlatAE encoder는 frozen).
+# 입력: backbone_features (VLM backbone 출력이 action_head 내부 vlln +
+#   vl_self_attention을 통과한 뒤의 processed VL 토큰) + backbone_attention_mask.
+#   -> action_head가 실제 정책 예측에 쓰는 것과 동일한 feature를 distill 대상으로 pool.
+# 출력: [B, 512] student 벡터 -> FlatAE unit_z [B,32]와 RKD distance/angle loss.
+# gradient 경로: student head -> vlln/vl_self_attention -> (backbone은 frozen).
+# 실측 배선: clvla/RKD_common/check/smoke_rkd_forward.py (student grad>0, teacher grad 0).
+
+# ==========================================================================
+# Defaults reference (commented = value used automatically when omitted).
+# 주석 처리하면 아래 값이 그대로 들어간다. 값 확인용이므로 켜지 말 것.
+# 출처: gr00t/configs/finetune_config.py, gr00t/configs/model/gr00t_n1d7.py.
+# --------------------------------------------------------------------------
+# --- training defaults (FinetuneConfig) ---
+# learning_rate: 1.0e-4
+# warmup_ratio: 0.05
+# weight_decay: 1.0e-5
+# gradient_accumulation_steps: 1
+# dataloader_num_workers: 2
+# episode_sampling_rate: 0.1
+# shard_size: 1024              # 2**10
+# num_shards_per_epoch: 100000  # int(1e5)
+# ds_weights_alpha:             # None
+# tune_llm: false
+# tune_visual: false
+# tune_projector: true
+# tune_diffusion_model: true
+# tune_vlln: true              # model config default (FinetuneConfig에는 없음)
+#
+# --- trainable 파라미터 (기본 tune_* 기준, 3B 실측) ---
+# TRAINABLE 1.62B / 3.14B = 51.5% (action head만; VLM backbone은 동결)
+#   action_head.model (DiT)          1.09B    <- tune_diffusion_model
+#   action_head.action_encoder       233M     <- tune_projector
+#   action_head.vl_self_attention    201M     <- tune_vlln
+#   action_head.state_encoder        55M      <- tune_projector
+#   action_head.action_decoder       38M      <- tune_projector
+#   action_head.position_embedding   1.6M     <- tune_projector
+#   action_head.vlln                 4K       <- tune_vlln
+# FROZEN 1.52B (48.5%): backbone 전체(Qwen3-VL LLM + vision), tune_llm/visual=F
+# 실측: clvla/RKD_common/check/probe_trainable.py
+# 이미지 전처리(crop/jitter)와 action 정규화는 학습 파라미터가 아니다(gradient 없음).
+# state_dropout_prob: 0.0       # FinetuneConfig default (model config default is 0.8)
+#
+# --- image augmentation defaults (base ckpt로부터 자동 적용) ---
+# 학습은 start_from_checkpoint(nvidia/GR00T-N1.7-3B)로부터
+# AutoProcessor.from_pretrained(..., overrides)로 프로세서를 만든다. YAML에서
+# 생략하면 launcher가 override로 None을 넘기는데, HF from_pretrained는 None을
+# "덮지 않음"으로 처리하므로 base 체크포인트에 저장된 값이 그대로 살아난다.
+# 즉 아래 jitter/rotation/crop은 안 넣어도 자동으로 들어간다(실측 확인).
+# color_jitter_params:          # base ckpt 값 자동 적용, train transform에 ColorJitter 포함
+#   brightness: 0.3
+#   contrast: 0.4
+#   saturation: 0.5
+#   hue: 0.08
+# random_rotation_angle: 0      # base ckpt 값(0)
+# crop_fraction: 0.95           # processor __init__ 기본값 -> 자동 적용
+# shortest_image_edge: 256      # processor __init__ 기본값 -> 자동 적용
+# image_crop_size:              # (효과 없음) from_pretrained 경로에선 crop_fraction/edge가 유효
+# image_target_size:            # (효과 없음)
+# use_albumentations_transforms: true
+#
+# 실측(clvla/RKD_common/check/probe_processor_aug.py, probe_crop_fraction.py):
+# jitter/crop 미기재 시 train transform =
+#   [SmallestMaxSize(256), FractionalRandomCrop(crop_fraction=0.95),
+#    SmallestMaxSize(256), ColorJitter]
+# color_jitter_params={brightness:0.3,contrast:0.4,saturation:0.5,hue:0.08},
+# crop_fraction=0.95, shortest_image_edge=256. 유효 crop 값은 model config의
+# image_crop_size(230/256)가 아니라 processor __init__ 기본(0.95/256)이다.
+# 끄거나 바꾸고 싶을 때만 명시적으로 재정의한다.

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/experiment_cfg/final_model_config.json

@@ -0,0 +1,88 @@
+{
+  "action_horizon": 40,
+  "add_pos_embed": true,
+  "apply_sincos_state_encoding": false,
+  "architectures": [
+    "Gr00tN1d7"
+  ],
+  "attn_dropout": 0.2,
+  "attn_implementation": null,
+  "backbone_embedding_dim": 2048,
+  "backbone_trainable_params_fp32": true,
+  "color_jitter_params": {
+    "brightness": 0.3,
+    "contrast": 0.4,
+    "hue": 0.08,
+    "saturation": 0.5
+  },
+  "crop_fraction": 0.95,
+  "diffusion_model_cfg": {
+    "attention_head_dim": 48,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "interleave_self_attention": true,
+    "norm_type": "ada_norm",
+    "num_attention_heads": 32,
+    "num_layers": 32,
+    "output_dim": 1024,
+    "positional_embeddings": null
+  },
+  "dtype": "float32",
+  "exclude_state": false,
+  "formalize_language": true,
+  "hidden_size": 1024,
+  "image_crop_size": [
+    230,
+    230
+  ],
+  "image_target_size": [
+    256,
+    256
+  ],
+  "letter_box_transform": false,
+  "load_bf16": false,
+  "max_action_dim": 132,
+  "max_num_embodiments": 32,
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rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/experiment_cfg/final_processor_config.json

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+      "saturation": 0.5,
+      "hue": 0.08
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+    "crop_fraction": 0.95,
+    "letter_box_transform": false,
+    "model_name": "nvidia/Cosmos-Reason2-2B",
+    "model_type": "qwen",
+    "formalize_language": true,
+    "max_state_dim": 132,
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+}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/rng_state.pth

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rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/scheduler.pt

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rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/training_args.bin

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rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/checkpoint-60000/wandb_config.json

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+{"project": "finetune-gr00t-n1d7", "run_id": "DTWSammon_UnitZ_DistanceOnly"}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/config.json

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+{
+  "action_horizon": 40,
+  "add_pos_embed": true,
+  "apply_sincos_state_encoding": false,
+  "architectures": [
+    "Gr00tN1d7"
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+  "attn_dropout": 0.2,
+  "attn_implementation": null,
+  "backbone_embedding_dim": 2048,
+  "backbone_trainable_params_fp32": true,
+  "color_jitter_params": {
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+    "contrast": 0.4,
+    "hue": 0.08,
+    "saturation": 0.5
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+  "diffusion_model_cfg": {
+    "attention_head_dim": 48,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "interleave_self_attention": true,
+    "norm_type": "ada_norm",
+    "num_attention_heads": 32,
+    "num_layers": 32,
+    "output_dim": 1024,
+    "positional_embeddings": null
+  },
+  "dtype": "float32",
+  "exclude_state": false,
+  "formalize_language": true,
+  "hidden_size": 1024,
+  "image_crop_size": [
+    230,
+    230
+  ],
+  "image_target_size": [
+    256,
+    256
+  ],
+  "letter_box_transform": false,
+  "load_bf16": false,
+  "max_action_dim": 132,
+  "max_num_embodiments": 32,
+  "max_seq_len": 1024,
+  "max_state_dim": 132,
+  "model_dtype": "bfloat16",
+  "model_name": "nvidia/Cosmos-Reason2-2B",
+  "model_type": "Gr00tN1d7",
+  "noise_beta_alpha": 1.5,
+  "noise_beta_beta": 1.0,
+  "noise_s": 0.999,
+  "num_inference_timesteps": 4,
+  "num_timestep_buckets": 1000,
+  "random_history_crop": true,
+  "random_rotation_angle": 0,
+  "reproject_vision": false,
+  "rtc_ramp_rate": 6.0,
+  "select_layer": 16,
+  "shortest_image_edge": 256,
+  "state_dropout_prob": 0.0,
+  "state_gaussian_noise_std": 0.0,
+  "transformers_version": "4.57.3",
+  "tune_diffusion_model": true,
+  "tune_linear": true,
+  "tune_llm": false,
+  "tune_projector": true,
+  "tune_top_llm_layers": 0,
+  "tune_visual": false,
+  "tune_vlln": true,
+  "use_albumentations": true,
+  "use_alternate_vl_dit": true,
+  "use_flash_attention": true,
+  "use_future_tokens": false,
+  "use_mean_std": false,
+  "use_percentiles": true,
+  "use_vl_self_attention": true,
+  "use_vlln": true,
+  "vl_self_attention_cfg": {
+    "attention_head_dim": 64,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "num_attention_heads": 32,
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+}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/experiment_cfg/config.yaml

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+# RoboCasa-Kitchen (v2) finetune with FlatAE-DTW-Sammon lambda=0.1 unit_z distance-only RKD teacher.
+#
+# Run:
+#   cd $PROJECT
+#   export LD_LIBRARY_PATH="$FFMPEG_LIB:$LD_LIBRARY_PATH"
+#   CUDA_VISIBLE_DEVICES=1 uv run python clvla/launch_finetune.py --config clvla/cfg/Robocasa-kitchen/Rel-AE/RKD_RawMu_DistanceOnly.yaml
+#
+# Any flag can be overridden on the CLI, e.g. append `--max-steps 1000`.
+# Keys are stock finetune flags (underscored); the `rkd:` block maps to --rkd-*.
+# ${HOME} and other env vars are expanded.
+
+# --- stock finetune flags ---
+base_model_path: nvidia/GR00T-N1.7-3B
+dataset_path: ${HOME}/robocasa_mg_gr00t_300
+embodiment_tag: new_embodiment
+modality_config_path: ${HOME}/clvla/benchmarks/Robocasa-Kitchen/robocasa_config.py
+output_dir: ${HOME}/groot_robocasa-kitchen/train_ckpt/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly
+experiment_name: DTWSammon_UnitZ_DistanceOnly
+global_batch_size: 64
+max_steps: 60000
+save_steps: 5000
+save_total_limit: 1
+num_gpus: 1            # RKD is single-GPU only
+# learning_rate: 1.0e-4
+# warmup_ratio: 0.05
+state_dropout_prob: 0.0
+use_percentiles: true
+use_wandb: true
+wandb_project: finetune-gr00t-n1d7
+
+# --- RKD add-on (maps to --rkd-*) ---
+rkd:
+  enabled: true
+  teacher_checkpoint: ${HOME}/groot_robocasa_V2/RelVAE_ckpt/mg_gr00t_300/flat_ae_dtw_z32_repraw_z_fh128_dh512_gw0p1/checkpoint-step-20000
+  latent_mode: unit_z
+  fm_weight: 1.0
+  loss_weight: 1.0
+  loss_weight_end: 0.0        # cosine ramp 1 -> 0 over max_steps
+  loss_weight_schedule: cosine
+  distance_weight: 1.0
+  angle_weight: 0.0
+  student_hidden: 512
+  student_output: 512
+  # max_steps omitted -> inherits --max-steps for the schedule denominator
+#
+# --- RKD student head 연결 지점 ---
+# RKD on이면 StudentAttentionHead(in=2048 backbone_embedding_dim, hidden/out=512)
+# 1개가 trainable로 추가된다(teacher FlatAE encoder는 frozen).
+# 입력: backbone_features (VLM backbone 출력이 action_head 내부 vlln +
+#   vl_self_attention을 통과한 뒤의 processed VL 토큰) + backbone_attention_mask.
+#   -> action_head가 실제 정책 예측에 쓰는 것과 동일한 feature를 distill 대상으로 pool.
+# 출력: [B, 512] student 벡터 -> FlatAE unit_z [B,32]와 RKD distance/angle loss.
+# gradient 경로: student head -> vlln/vl_self_attention -> (backbone은 frozen).
+# 실측 배선: clvla/RKD_common/check/smoke_rkd_forward.py (student grad>0, teacher grad 0).
+
+# ==========================================================================
+# Defaults reference (commented = value used automatically when omitted).
+# 주석 처리하면 아래 값이 그대로 들어간다. 값 확인용이므로 켜지 말 것.
+# 출처: gr00t/configs/finetune_config.py, gr00t/configs/model/gr00t_n1d7.py.
+# --------------------------------------------------------------------------
+# --- training defaults (FinetuneConfig) ---
+# learning_rate: 1.0e-4
+# warmup_ratio: 0.05
+# weight_decay: 1.0e-5
+# gradient_accumulation_steps: 1
+# dataloader_num_workers: 2
+# episode_sampling_rate: 0.1
+# shard_size: 1024              # 2**10
+# num_shards_per_epoch: 100000  # int(1e5)
+# ds_weights_alpha:             # None
+# tune_llm: false
+# tune_visual: false
+# tune_projector: true
+# tune_diffusion_model: true
+# tune_vlln: true              # model config default (FinetuneConfig에는 없음)
+#
+# --- trainable 파라미터 (기본 tune_* 기준, 3B 실측) ---
+# TRAINABLE 1.62B / 3.14B = 51.5% (action head만; VLM backbone은 동결)
+#   action_head.model (DiT)          1.09B    <- tune_diffusion_model
+#   action_head.action_encoder       233M     <- tune_projector
+#   action_head.vl_self_attention    201M     <- tune_vlln
+#   action_head.state_encoder        55M      <- tune_projector
+#   action_head.action_decoder       38M      <- tune_projector
+#   action_head.position_embedding   1.6M     <- tune_projector
+#   action_head.vlln                 4K       <- tune_vlln
+# FROZEN 1.52B (48.5%): backbone 전체(Qwen3-VL LLM + vision), tune_llm/visual=F
+# 실측: clvla/RKD_common/check/probe_trainable.py
+# 이미지 전처리(crop/jitter)와 action 정규화는 학습 파라미터가 아니다(gradient 없음).
+# state_dropout_prob: 0.0       # FinetuneConfig default (model config default is 0.8)
+#
+# --- image augmentation defaults (base ckpt로부터 자동 적용) ---
+# 학습은 start_from_checkpoint(nvidia/GR00T-N1.7-3B)로부터
+# AutoProcessor.from_pretrained(..., overrides)로 프로세서를 만든다. YAML에서
+# 생략하면 launcher가 override로 None을 넘기는데, HF from_pretrained는 None을
+# "덮지 않음"으로 처리하므로 base 체크포인트에 저장된 값이 그대로 살아난다.
+# 즉 아래 jitter/rotation/crop은 안 넣어도 자동으로 들어간다(실측 확인).
+# color_jitter_params:          # base ckpt 값 자동 적용, train transform에 ColorJitter 포함
+#   brightness: 0.3
+#   contrast: 0.4
+#   saturation: 0.5
+#   hue: 0.08
+# random_rotation_angle: 0      # base ckpt 값(0)
+# crop_fraction: 0.95           # processor __init__ 기본값 -> 자동 적용
+# shortest_image_edge: 256      # processor __init__ 기본값 -> 자동 적용
+# image_crop_size:              # (효과 없음) from_pretrained 경로에선 crop_fraction/edge가 유효
+# image_target_size:            # (효과 없음)
+# use_albumentations_transforms: true
+#
+# 실측(clvla/RKD_common/check/probe_processor_aug.py, probe_crop_fraction.py):
+# jitter/crop 미기재 시 train transform =
+#   [SmallestMaxSize(256), FractionalRandomCrop(crop_fraction=0.95),
+#    SmallestMaxSize(256), ColorJitter]
+# color_jitter_params={brightness:0.3,contrast:0.4,saturation:0.5,hue:0.08},
+# crop_fraction=0.95, shortest_image_edge=256. 유효 crop 값은 model config의
+# image_crop_size(230/256)가 아니라 processor __init__ 기본(0.95/256)이다.
+# 끄거나 바꾸고 싶을 때만 명시적으로 재정의한다.

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/experiment_cfg/final_model_config.json

@@ -0,0 +1,88 @@
+{
+  "action_horizon": 40,
+  "add_pos_embed": true,
+  "apply_sincos_state_encoding": false,
+  "architectures": [
+    "Gr00tN1d7"
+  ],
+  "attn_dropout": 0.2,
+  "attn_implementation": null,
+  "backbone_embedding_dim": 2048,
+  "backbone_trainable_params_fp32": true,
+  "color_jitter_params": {
+    "brightness": 0.3,
+    "contrast": 0.4,
+    "hue": 0.08,
+    "saturation": 0.5
+  },
+  "crop_fraction": 0.95,
+  "diffusion_model_cfg": {
+    "attention_head_dim": 48,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "interleave_self_attention": true,
+    "norm_type": "ada_norm",
+    "num_attention_heads": 32,
+    "num_layers": 32,
+    "output_dim": 1024,
+    "positional_embeddings": null
+  },
+  "dtype": "float32",
+  "exclude_state": false,
+  "formalize_language": true,
+  "hidden_size": 1024,
+  "image_crop_size": [
+    230,
+    230
+  ],
+  "image_target_size": [
+    256,
+    256
+  ],
+  "letter_box_transform": false,
+  "load_bf16": false,
+  "max_action_dim": 132,
+  "max_num_embodiments": 32,
+  "max_seq_len": 1024,
+  "max_state_dim": 132,
+  "model_dtype": "bfloat16",
+  "model_name": "nvidia/Cosmos-Reason2-2B",
+  "model_type": "Gr00tN1d7",
+  "noise_beta_alpha": 1.5,
+  "noise_beta_beta": 1.0,
+  "noise_s": 0.999,
+  "num_inference_timesteps": 4,
+  "num_timestep_buckets": 1000,
+  "random_history_crop": true,
+  "random_rotation_angle": 0,
+  "reproject_vision": false,
+  "rtc_ramp_rate": 6.0,
+  "select_layer": 16,
+  "shortest_image_edge": 256,
+  "state_dropout_prob": 0.0,
+  "state_gaussian_noise_std": 0.0,
+  "transformers_version": "4.57.3",
+  "tune_diffusion_model": true,
+  "tune_linear": true,
+  "tune_llm": false,
+  "tune_projector": true,
+  "tune_top_llm_layers": 0,
+  "tune_visual": false,
+  "tune_vlln": true,
+  "use_albumentations": true,
+  "use_alternate_vl_dit": true,
+  "use_flash_attention": true,
+  "use_future_tokens": false,
+  "use_mean_std": false,
+  "use_percentiles": true,
+  "use_vl_self_attention": true,
+  "use_vlln": true,
+  "vl_self_attention_cfg": {
+    "attention_head_dim": 64,
+    "dropout": 0.2,
+    "final_dropout": true,
+    "num_attention_heads": 32,
+    "num_layers": 4,
+    "positional_embeddings": null
+  }
+}

rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/experiment_cfg/final_processor_config.json

@@ -0,0 +1,1148 @@
+{
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+  "processor_kwargs": {
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+              "type": "EEF",
+              "format": "XYZ_ROT6D",
+              "state_key": "right_wrist_eef"
+            },
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+              "rep": "ABSOLUTE",
+              "type": "NON_EEF",
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+}

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rkd/Rel-AE/DTW-Sammon/UnitZ/DistanceOnly/wandb_config.json

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