config.yaml

resume: false
device: cuda
use_amp: true
seed: 100000
dataset_repo_id: lerobot/aloha_sim_insertion_human
video_backend: pyav
training:
  offline_steps: 100000
  num_workers: 12
  batch_size: 12
  eval_freq: 10000
  log_freq: 1000
  save_checkpoint: true
  save_freq: 10000
  online_steps: 0
  online_rollout_n_episodes: 1
  online_rollout_batch_size: 1
  online_steps_between_rollouts: 1
  online_sampling_ratio: 0.5
  online_env_seed: null
  online_buffer_capacity: null
  online_buffer_seed_size: 0
  do_online_rollout_async: false
  image_transforms:
    enable: false
    max_num_transforms: 3
    random_order: false
    brightness:
      weight: 1
      min_max:
      - 0.8
      - 1.2
    contrast:
      weight: 1
      min_max:
      - 0.8
      - 1.2
    saturation:
      weight: 1
      min_max:
      - 0.5
      - 1.5
    hue:
      weight: 1
      min_max:
      - -0.05
      - 0.05
    sharpness:
      weight: 1
      min_max:
      - 0.8
      - 1.2
  save_model: true
  grad_clip_norm: 50
  lr: 3.0e-05
  min_lr: 1.0e-05
  lr_cycle_steps: 100000
  weight_decay: 1.0e-05
  delta_timestamps:
    observation.images.top:
    - -0.7
    - -0.68
    - -0.66
    - -0.64
    - -0.62
    - -0.6
    - -0.58
    - -0.56
    - -0.54
    - -0.52
    - -0.5
    - -0.02
    - 0.0
    observation.state:
    - -0.7
    - -0.68
    - -0.66
    - -0.64
    - -0.62
    - -0.6
    - -0.58
    - -0.56
    - -0.54
    - -0.52
    - -0.5
    - -0.02
    - 0.0
    action:
    - -0.7
    - -0.68
    - -0.66
    - -0.64
    - -0.62
    - -0.6
    - -0.58
    - -0.56
    - -0.54
    - -0.52
    - -0.5
    - -0.02
    - 0.0
    - 0.02
    - 0.04
    - 0.06
    - 0.08
    - 0.1
    - 0.12
    - 0.14
    - 0.16
    - 0.18
    - 0.2
    - 0.22
    - 0.24
    - 0.26
    - 0.28
    - 0.3
    - 0.32
    - 0.34
    - 0.36
    - 0.38
    - 0.4
    - 0.42
    - 0.44
    - 0.46
    - 0.48
    - 0.5
    - 0.52
    - 0.54
    - 0.56
    - 0.58
    - 0.6
    - 0.62
    - 0.64
    - 0.66
    - 0.68
    - 0.7
    - 0.72
    - 0.74
    - 0.76
    - 0.78
    - 0.8
    - 0.82
    - 0.84
    - 0.86
    - 0.88
    - 0.9
    - 0.92
    - 0.94
    - 0.96
    - 0.98
    - 1.0
    - 1.02
    - 1.04
    - 1.06
    - 1.08
    - 1.1
    - 1.12
    - 1.14
    - 1.16
    - 1.18
    - 1.2
    - 1.22
    - 1.24
    - 1.26
    - 1.28
    - 1.3
    - 1.32
    - 1.34
    - 1.36
    - 1.38
    - 1.4
    - 1.42
    - 1.44
    - 1.46
    - 1.48
    - 1.5
    - 1.52
    - 1.54
    - 1.56
    - 1.58
    - 1.6
    - 1.62
    - 1.64
    - 1.66
    - 1.68
    - 1.7
    - 1.72
    - 1.74
    - 1.76
    - 1.78
    - 1.8
    - 1.82
    - 1.84
    - 1.86
    - 1.88
    - 1.9
    - 1.92
    - 1.94
    - 1.96
    - 1.98
    - 2.0
    - 2.02
    - 2.04
    - 2.06
    - 2.08
    - 2.1
    - 2.12
    - 2.14
    - 2.16
    - 2.18
    - 2.2
    - 2.22
    - 2.24
    - 2.26
    - 2.28
    - 2.3
    - 2.32
    - 2.34
    - 2.36
    - 2.38
    - 2.4
    - 2.42
    - 2.44
    - 2.46
    - 2.48
    - 2.5
    - 2.52
    - 2.54
    - 2.56
    - 2.58
    - 2.6
    - 2.62
    - 2.64
    - 2.66
    - 2.68
    - 2.7
    - 2.72
    - 2.74
    - 2.76
    - 2.78
    - 2.8
    - 2.82
    - 2.84
    - 2.86
    - 2.88
    - 2.9
    - 2.92
    - 2.94
    - 2.96
    - 2.98
eval:
  n_episodes: 50
  batch_size: 10
  use_async_envs: false
wandb:
  enable: true
  disable_artifact: false
  project: insert
  notes: ''
fps: 50
env:
  name: aloha
  task: AlohaInsertion-v0
  state_dim: 14
  action_dim: 14
  fps: ${fps}
  episode_length: 500
  gym:
    obs_type: pixels_agent_pos
    render_mode: rgb_array
override_dataset_stats:
  observation.images.top:
    mean:
    - - - 0.485
    - - - 0.456
    - - - 0.406
    std:
    - - - 0.229
    - - - 0.224
    - - - 0.225
policy:
  name: dot
  n_obs_steps: 3
  train_horizon: 150
  inference_horizon: 100
  lookback_obs_steps: 30
  lookback_aug: 5
  input_shapes:
    observation.images.top:
    - 3
    - 480
    - 640
    observation.state:
    - ${env.state_dim}
  output_shapes:
    action:
    - ${env.action_dim}
  input_normalization_modes:
    observation.images.top: mean_std
    observation.state: min_max
  output_normalization_modes:
    action: min_max
  vision_backbone: resnet18
  pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
  rescale_shape:
  - 480
  - 640
  lora_rank: 20
  merge_lora: true
  crop_scale: 0.8
  state_noise: 0.01
  noise_decay: 0.999995
  pre_norm: true
  dim_model: 128
  n_heads: 8
  dim_feedforward: 512
  n_decoder_layers: 8
  dropout: 0.1
  alpha: 0.98
  train_alpha: 0.99
  predict_every_n: 1
  return_every_n: 1