lerobot/lerobot/configs/policy/act.yaml

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1.4 KiB
YAML

# @package _global_
offline_steps: 80000
online_steps: 0
eval_episodes: 1
eval_freq: 10000
save_freq: 100000
log_freq: 250
n_obs_steps: 1
# when temporal_agg=False, n_action_steps=horizon
# See `configuration_act.py` for more details.
policy:
name: act
pretrained_model_path:
# Environment.
# Inherit these from the environment config.
state_dim: ???
action_dim: ???
# Inputs / output structure.
n_obs_steps: ${n_obs_steps}
camera_names: [top] # [top, front_close, left_pillar, right_pillar]
chunk_size: 100 # chunk_size
n_action_steps: 100
# Vision preprocessing.
image_normalization_mean: [0.485, 0.456, 0.406]
image_normalization_std: [0.229, 0.224, 0.225]
# Architecture.
# Vision backbone.
vision_backbone: resnet18
use_pretrained_backbone: true
replace_final_stride_with_dilation: false
# Transformer layers.
pre_norm: false
d_model: 512
n_heads: 8
dim_feedforward: 3200
feedforward_activation: relu
n_encoder_layers: 4
n_decoder_layers: 1
# VAE.
use_vae: true
latent_dim: 32
n_vae_encoder_layers: 4
# Inference.
use_temporal_aggregation: false
# Training and loss computation.
dropout: 0.1
kl_weight: 10.0
# ---
# TODO(alexander-soare): Remove these from the policy config.
batch_size: 8
lr: 1e-5
lr_backbone: 1e-5
weight_decay: 1e-4
grad_clip_norm: 10
utd: 1
delta_timestamps:
action: "[i / ${fps} for i in range(${policy.chunk_size})]"