WIP: 2024_07_16_vqbet_koch_pick_place_lego_simple_v2

lerobot/configs/policy/vqbet_koch_real.yaml

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+# @package _global_
+
+# Defaults for training for the PushT dataset.
+
+seed: 100000
+dataset_repo_id: lerobot/koch_pick_place_lego
+
+override_dataset_stats:
+  observation.images.laptop:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+  observation.images.phone:
+    # stats from imagenet, since we use a pretrained vision model
+    mean: [[[0.485]], [[0.456]], [[0.406]]]  # (c,1,1)
+    std: [[[0.229]], [[0.224]], [[0.225]]]  # (c,1,1)
+
+training:
+  offline_steps: 80000
+  online_steps: 0
+  eval_freq: -1
+  save_freq: 10000
+  save_checkpoint: true
+
+  batch_size: 8
+  grad_clip_norm: 10
+  lr: 1.0e-4
+  lr_scheduler: cosine
+  lr_warmup_steps: 2000
+  adam_betas: [0.95, 0.999]
+  adam_eps: 1.0e-8
+  adam_weight_decay: 1.0e-6
+  online_steps_between_rollouts: 1
+
+  # VQ-BeT specific
+  vqvae_lr: 1.0e-3
+  n_vqvae_training_steps: 20000
+  bet_weight_decay: 2e-4
+  bet_learning_rate: 5.5e-5
+  bet_betas: [0.9, 0.999]
+
+  delta_timestamps:
+    observation.images.laptop: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    observation.images.phone: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    observation.state: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, 1)]"
+    action: "[i / ${fps} for i in range(1 - ${policy.n_obs_steps}, ${policy.n_action_pred_token} + ${policy.action_chunk_size} - 1)]"
+
+eval:
+  n_episodes: 50
+  batch_size: 50
+
+policy:
+  name: vqbet
+
+  # Input / output structure.
+  n_obs_steps: 5
+  n_action_pred_token: 7
+  action_chunk_size: 5
+
+  input_shapes:
+    # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
+    observation.images.laptop: [3, 480, 640]
+    observation.images.phone: [3, 480, 640]
+    observation.state: ["${env.state_dim}"]
+  output_shapes:
+    action: ["${env.action_dim}"]
+
+  # Normalization / Unnormalization
+  input_normalization_modes:
+    observation.images.laptop: mean_std
+    observation.images.phone: mean_std
+    observation.state: min_max
+  output_normalization_modes:
+    action: min_max
+
+  # Architecture / modeling.
+  # Vision backbone.
+  vision_backbone: resnet18
+  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
+  crop_is_random: False
+  spatial_softmax_num_keypoints: 512
+  use_group_norm: False
+  crop_shape: null
+  # VQ-VAE
+  n_vqvae_training_steps: ${training.n_vqvae_training_steps}
+  vqvae_n_embed: 16
+  vqvae_embedding_dim: 256
+  vqvae_enc_hidden_dim: 128
+  # VQ-BeT
+  gpt_block_size: 500
+  gpt_input_dim: 512
+  gpt_output_dim: 512
+  gpt_n_layer: 8
+  gpt_n_head: 8
+  gpt_hidden_dim: 512
+  dropout: 0.1
+  mlp_hidden_dim: 1024
+  offset_loss_weight: 10000.
+  primary_code_loss_weight: 5.0
+  secondary_code_loss_weight: 0.5
+  bet_softmax_temperature: 0.1
+  sequentially_select: False